人工智能培训

人工智能培训

人工智能培训,AI培训,Artificial Intelligence培训

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人工智能大纲

代码 名字 时长 概览
iotemi IoT (Internet of Things) for Entrepreneurs, Managers and Investors 21小时 Unlike other technologies, IoT is far more complex encompassing almost every branch of core Engineering-Mechanical, Electronics, Firmware, Middleware, Cloud, Analytics and Mobile. For each of its engineering layers, there are aspects of economics, standards, regulations and evolving state of the art. This is for the firs time, a modest course is offered to cover all of these critical aspects of IoT Engineering. Summary An advanced training program covering the current state of the art in Internet of Things Cuts across multiple technology domains to develop awareness of an IoT system and its components and how it can help businesses and organizations. Live demo of model IoT applications to showcase practical IoT deployments across different industry domains, such as Industrial IoT, Smart Cities, Retail, Travel & Transportation and use cases around connected devices & things Target Audience Managers responsible for business and operational processes within their respective organizations and want to know how to harness IoT to make their systems and processes more efficient. Entrepreneurs and Investors who are looking to build new ventures and want to develop a better understanding of the IoT technology landscape to see how they can leverage it in an effective manner. Estimates for Internet of Things or IoT market value are massive, since by definition the IoT is an integrated and diffused layer of devices, sensors, and computing power that overlays entire consumer, business-to-business, and government industries. The IoT will account for an increasingly huge number of connections: 1.9 billion devices today, and 9 billion by 2018. That year, it will be roughly equal to the number of smartphones, smart TVs, tablets, wearable computers, and PCs combined. In the consumer space, many products and services have already crossed over into the IoT, including kitchen and home appliances, parking, RFID, lighting and heating products, and a number of applications in Industrial Internet. However, the underlying technologies of IoT are nothing new as M2M communication existed since the birth of Internet. However what changed in last couple of years is the emergence of number of inexpensive wireless technologies added by overwhelming adaptation of smart phones and Tablet in every home. Explosive growth of mobile devices led to present demand of IoT. Due to unbounded opportunities in IoT business, a large number of small and medium sized entrepreneurs jumped on a bandwagon of IoT gold rush. Also due to emergence of open source electronics and IoT platform, cost of development of IoT system and further managing its sizable production is increasingly affordable. Existing electronic product owners are experiencing pressure to integrate their device with Internet or Mobile app. This training is intended for a technology and business review of an emerging industry so that IoT enthusiasts/entrepreneurs can grasp the basics of IoT technology and business. Course Objective Main objective of the course is to introduce emerging technological options, platforms and case studies of IoT implementation in home & city automation (smart homes and cities), Industrial Internet, healthcare, Govt., Mobile Cellular and other areas. Basic introduction of all the elements of IoT-Mechanical, Electronics/sensor platform, Wireless and wireline protocols, Mobile to Electronics integration, Mobile to enterprise integration, Data-analytics and Total control plane M2M Wireless protocols for IoT- WiFi, Zigbee/Zwave, Bluetooth, ANT+ : When and where to use which one? Mobile/Desktop/Web app- for registration, data acquisition and control –Available M2M data acquisition platform for IoT-–Xively, Omega and NovoTech, etc. Security issues and security solutions for IoT Open source/commercial electronics platform for IoT-Raspberry Pi, Arduino , ArmMbedLPC etc Open source /commercial enterprise cloud platform for AWS-IoT apps, Azure -IOT, Watson-IOT cloud in addition to other minor IoT clouds Studies of business and technology of some of the common IoT devices like Home automation, Smoke alarm, vehicles, military, home health etc.
dataminr Data Mining with R 14小时 R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.
dataar Data Analytics With R 21小时 R is a very popular, open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students.  It covers language fundamentals, libraries and advanced concepts.  Advanced data analytics and graphing with real world data. Audience Developers / data analytics Duration 3 days Format Lectures and Hands-on
spmllib Apache Spark MLlib 35小时 MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs. It divides into two packages: spark.mllib contains the original API built on top of RDDs. spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines.   Audience This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark
IntroToAvro Apache Avro: Data serialization for distributed applications 14小时 This course is intended for Developers Format of the course Lectures, hands-on practice, small tests along the way to gauge understanding
druid Druid: Build a fast, real-time data analysis system 21小时 Druid is an open-source, column-oriented, distributed data store written in Java. It was designed to quickly ingest massive quantities of event data and execute low-latency OLAP queries on that data. Druid is commonly used in business intelligence applications to analyze high volumes of real-time and historical data. It is also well suited for powering fast, interactive, analytic dashboards for end-users. Druid is used by companies such as Alibaba, Airbnb, Cisco, eBay, Netflix, Paypal, and Yahoo. In this course we explore some of the limitations of data warehouse solutions and discuss how Druid can compliment those technologies to form a flexible and scalable streaming analytics stack. We walk through many examples, offering participants the chance to implement and test Druid-based solutions in a lab environment. Audience     Application developers     Software engineers     Technical consultants     DevOps professionals     Architecture engineers Format of the course     Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
glusterfs GlusterFS for System Administrators 21小时 GlusterFS is an open-source distributed file storage system that can scale up to petabytes of capacity. GlusterFS is designed to provide additional space depending on the user's storage requirements. A common application for GlusterFS is cloud computing storage systems. In this instructor-led training, participants will learn how to use normal, off-the-shelf hardware to create and deploy a storage system that is scalable and always available.  By the end of the course, participants will be able to: Install, configure, and maintain a full-scale GlusterFS system. Implement large-scale storage systems in different types of environments. Audience System administrators Storage administrators Format of the Course Part lecture, part discussion, exercises and heavy hands-on practice.
vespa Vespa: Serving large-scale data in real-time 14小时 Vespa an open-source big data processing and serving engine created by Yahoo.  It is used to respond to user queries, make recommendations, and provide personalized content and advertisements in real-time. This instructor-led, live training introduces the challenges of serving large-scale data and walks participants through the creation of an application that can compute responses to user requests, over large datasets in real-time. By the end of this training, participants will be able to: Use Vespa to quickly compute data (store, search, rank, organize) at serving time while a user waits Implement Vespa into existing applications involving feature search, recommendations, and personalization Integrate and deploy Vespa with existing big data systems such as Hadoop and Storm. Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
pythonadvml Python用于高级机器学习 21小时 在这一由讲师引导的现场培训中,参与者将学习Python中最相关及最尖端的机器学习技术,因为它们构建了一系列涉及图像、音乐、文本和财务数据的演示应用程序。 在本次培训结束后,参与者将能够: 运用用于解决复杂问题的机器学习算法和技术 将深度学习和半监督学习应用于涉及图像、音乐、文本和财务数据的应用程序 推动Python算法达到其最大潜力 使用例如NumPy和Theano的库和包 受众 开发人员 分析师 数据科学家 课程形式 部分讲座、部分讨论、练习和大量实操
matlabdl Matlab:用于深度学习 14小时 在这一由讲师引导的现场培训中,参与者将学习如何使用Matlab来设计、构建、可视化用于图像识别的卷积神经网络。 在培训结束后,参与者将能够: 建立深度学习的模式 使数据分类自动化 使用Caffe和TensorFlow-Keras的模型 使用多个GPU、云或群集训练数据 受众 开发人员 工程师 领域专家 课程形式 部分讲座、部分讨论、练习和大量实操
botsazure Developing Intelligent Bots with Azure 14小时 The Azure Bot Service combines the power of the Microsoft Bot Framework and Azure functions to enable rapid development of intelligent bots. In this instructor-led, live training, participants will learn how to easily create an intelligent bot using Microsoft Azure By the end of this training, participants will be able to: Learn the fundamentals of intelligent bots Learn how to create intelligent bots using cloud applications Understand how to use the Microsoft Bot Framework, the Bot Builder SDK, and the Azure Bot Service Understand how to design bots using bot patterns Develop their first intelligent bot using Microsoft Azure Audience Developers Hobbyists Engineers IT Professionals Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
echarts ECharts 14小时 eCharts is a free JavaScript library used for interactive charting and data visualization. In this instructor-led, live training, participants will learn the fundamental functionalities of ECharts as they step through the process of creating and configuring charts using ECharts. By the end of this training, participants will be able to: Understand the fundamentals of ECharts Explore and utilize the various features and configuration options in ECharts Build their own simple, interactive, and responsive charts with ECharts Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
bdbitcsp 为电信服务供应商的智能大数据信息业务 35小时 Overview Communications service providers (CSP) are facing pressure to reduce costs and maximize average revenue per user (ARPU), while ensuring an excellent customer experience, but data volumes keep growing. Global mobile data traffic will grow at a compound annual growth rate (CAGR) of 78 percent to 2016, reaching 10.8 exabytes per month. Meanwhile, CSPs are generating large volumes of data, including call detail records (CDR), network data and customer data. Companies that fully exploit this data gain a competitive edge. According to a recent survey by The Economist Intelligence Unit, companies that use data-directed decision-making enjoy a 5-6% boost in productivity. Yet 53% of companies leverage only half of their valuable data, and one-fourth of respondents noted that vast quantities of useful data go untapped. The data volumes are so high that manual analysis is impossible, and most legacy software systems can’t keep up, resulting in valuable data being discarded or ignored. With Big Data & Analytics’ high-speed, scalable big data software, CSPs can mine all their data for better decision making in less time. Different Big Data products and techniques provide an end-to-end software platform for collecting, preparing, analyzing and presenting insights from big data. Application areas include network performance monitoring, fraud detection, customer churn detection and credit risk analysis. Big Data & Analytics products scale to handle terabytes of data but implementation of such tools need new kind of cloud based database system like Hadoop or massive scale parallel computing processor ( KPU etc.) This course work on Big Data BI for Telco covers all the emerging new areas in which CSPs are investing for productivity gain and opening up new business revenue stream. The course will provide a complete 360 degree over view of Big Data BI in Telco so that decision makers and managers can have a very wide and comprehensive overview of possibilities of Big Data BI in Telco for productivity and revenue gain. Course objectives Main objective of the course is to introduce new Big Data business intelligence techniques in 4 sectors of Telecom Business (Marketing/Sales, Network Operation, Financial operation and Customer Relation Management). Students will be introduced to following: Introduction to Big Data-what is 4Vs (volume, velocity, variety and veracity) in Big Data- Generation, extraction and management from Telco perspective How Big Data analytic differs from legacy data analytic In-house justification of Big Data -Telco perspective Introduction to Hadoop Ecosystem- familiarity with all Hadoop tools like Hive, Pig, SPARC –when and how they are used to solve Big Data problem How Big Data is extracted to analyze for analytics tool-how Business Analysis’s can reduce their pain points of collection and analysis of data through integrated Hadoop dashboard approach Basic introduction of Insight analytics, visualization analytics and predictive analytics for Telco Customer Churn analytic and Big Data-how Big Data analytic can reduce customer churn and customer dissatisfaction in Telco-case studies Network failure and service failure analytics from Network meta-data and IPDR Financial analysis-fraud, wastage and ROI estimation from sales and operational data Customer acquisition problem-Target marketing, customer segmentation and cross-sale from sales data Introduction and summary of all Big Data analytic products and where they fit into Telco analytic space Conclusion-how to take step-by-step approach to introduce Big Data Business Intelligence in your organization Target Audience Network operation, Financial Managers, CRM managers and top IT managers in Telco CIO office. Business Analysts in Telco CFO office managers/analysts Operational managers QA managers
manbrphp Managing Business Rules with PHP Business Rules 14小时 This course explain how to write declarative rules using PHP Business Rules (http://sourceforge.net/projects/phprules/). It shows how to write, organize and integrate rules with existing code. Most of the course is based on exercises preceded with short introduction and examples.
hadoopadm1 Hadoop For Administrators 21小时 Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. In this three (optionally, four) days course, attendees will learn about the business benefits and use cases for Hadoop and its ecosystem, how to plan cluster deployment and growth, how to install, maintain, monitor, troubleshoot and optimize Hadoop. They will also practice cluster bulk data load, get familiar with various Hadoop distributions, and practice installing and managing Hadoop ecosystem tools. The course finishes off with discussion of securing cluster with Kerberos. “…The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized” — Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising Audience Hadoop administrators Format Lectures and hands-on labs, approximate balance 60% lectures, 40% labs.
aiintrozero From Zero to AI 35小时 This course is created for people who have no previous experience in probability and statistics.
Piwik Getting started with Piwik 21小时 Audience Web analysist Data analysists Market researchers Marketing and sales professionals System administrators Format of course     Part lecture, part discussion, heavy hands-on practice
nlpwithr NLP: Natural Language Processing with R 21小时 It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data. This course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements. By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance. Audience     Linguists and programmers Format of the course     Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
kylin Apache Kylin: From classic OLAP to real-time data warehouse 14小时 Apache Kylin is an extreme, distributed analytics engine for big data. In this instructor-led live training, participants will learn how to use Apache Kylin to set up a real-time data warehouse. By the end of this training, participants will be able to: Consume real-time streaming data using Kylin Utilize Apache Kylin's powerful features, including snowflake schema support, a rich SQL interface, spark cubing and subsecond query latency Note We use the latest version of Kylin (as of this writing, Apache Kylin v2.0) Audience Big data engineers Big Data analysts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
ApacheIgnite Apache Ignite: Improve speed, scale and availability with in-memory computing 14小时 Apache Ignite is an in-memory computing platform that sits between the application and data layer to improve speed, scale and availability. In this instructor-led, live training, participants will learn the principles behind persistent and pure in-memory storage as they step through the creation of a sample in-memory computing project. By the end of this training, participants will be able to: Use Ignite for in-memory, on-disk persistence as well as a purely distributed in-memory database Achieve persistence without syncing data back to a relational database Use Ignite to carry out SQL and distributed joins Improve performance by moving data closer to the CPU, using RAM as a storage Spread data sets across a cluster to achieve horizontal scalability Integrate Ignite with RDBMS, NoSQL, Hadoop and machine learning processors Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
radvml Advanced Machine Learning with R 21小时 In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application. By the end of this training, participants will be able to: Use techniques as hyper-parameter tuning and deep learning Understand and implement unsupervised learning techniques Put a model into production for use in a larger application Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
powerbiforbiandanalytics Power BI for Business Analysts 21小时 Microsoft Power BI is a free Software as a Service (SaaS) suite for analyzing data and sharing insights. Power BI dashboards provide a 360-degree view of the most important metrics in one place, updated in real time, and available on all of their devices. In this instructor-led, live training, participants will learn how to use Microsoft Power Bi to analyze and visualize data using a series of sample data sets. By the end of this training, participants will be able to: Create visually compelling dashboards that provide valuable insights into data Obtain and integrate data from multiple data sources Build and share visualizations with team members Adjust data with Power BI Desktop Audience Business managers Business analystss Data analysts Business Intelligence (BI) and Data Warehouse (DW) teams Report developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice  
devbot Developing a Bot 14小时 A bot or chatbot is like a computer assistant that is used to automate user interactions on various messaging platforms and get things done faster without the need for users to speak to another human. In this instructor-led, live training, participants will learn how to get started in developing a bot as they step through the creation of sample chatbots using bot development tools and frameworks. By the end of this training, participants will be able to: Understand the different uses and applications of bots Understand the complete process in developing bots Explore the different tools and platforms used in building bots Build a sample chatbot for Facebook Messenger Build a sample chatbot using Microsoft Bot Framework Audience Developers interested in creating their own bot Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
drlpython Deep Reinforcement Learning with Python 21小时 Deep Reinforcement Learning refers to the ability of an "artificial agents" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches. In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent. By the end of this training, participants will be able to: Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning Apply advanced Reinforcement Learning algorithms to solve real-world problems Build a Deep Learning Agent Audience Developers Data Scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
apacheh Administrator Training for Apache Hadoop 35小时 Audience: The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment Goal: Deep knowledge on Hadoop cluster administration.
mdldromgdmn Modelling Decision and Rules with OMG DMN 14小时 This course teaches how to design and execute decisions in rules with OMG DMN (Decision Model and Notation) standard.
solrdev Solr for Developers 21小时 This course introduces students to the Solr platform. Through a combination of lecture, discussion and labs students will gain hands on experience configuring effective search and indexing. The class begins with basic Solr installation and configuration then teaches the attendees the search features of Solr. Students will gain experience with faceting, indexing and search relevance among other features central to the Solr platform. The course wraps up with a number of advanced topics including spell checking, suggestions, Multicore and SolrCloud. Duration: 3 days Audience: Developers, business users, administrators
singa Mastering Apache SINGA 21小时 SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning. Audience This course is directed at researchers, engineers and developers seeking to utilize Apache SINGA as a deep learning framework. After completing this course, delegates will: understand SINGA’s structure and deployment mechanisms be able to carry out installation / production environment / architecture tasks and configuration be able to assess code quality, perform debugging, monitoring be able to implement advanced production like training models, embedding terms, building graphs and logging  
DM7 Getting started with DM7 (达梦7) 21小时 Audience Beginner or intermediate database developers Beginner or intermediate database administrators Programmers Format of the course Heavy emphasis on hands-on practice. Most of the concepts are learned through samples, exercises and hands-on development
voldemort Voldemort: Setting up a key-value distributed data store 14小时 Voldemort is an open-source distributed data store that is designed as a key-value store.  It is used at LinkedIn by numerous critical services powering a large portion of the site. This course will introduce the architecture and capabilities of Voldomort and walk participants through the setup and application of a key-value distributed data store. Audience     Software developers     System administrators     DevOps engineers Format of the course     Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
matlabfundamentalsfinance MATLAB基础 + MATLAB用于财务工作 35小时 本课程提供了MATLAB技术计算环境的全面介绍,及使用MATLAB进行金融应用的介绍。本课程面向初级使用者及有意复习相关知识的使用者。不需有编程经验或MATLAB知识。在整个课程中探讨数据分析、可视化、建模、编程等主题。主题包括: 使用MATLAB用户界面 输入命令并创建变量 分析向量和矩阵 可视化矢量和矩阵数据 使用数据文件 处理数据类型 使用脚本自动执行命令 用逻辑和流控制编写程序 写作功能 使用金融工具箱进行定量分析
snorkel Snorkel: Rapidly process training data 7小时 Snorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain. In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel. By the end of this training, participants will be able to: Programmatically create training sets to enable the labeling of massive training sets Train high-quality end models by first modeling noisy training sets Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
encogintro Encog: Introduction to Machine Learning 14小时 Encog is an open-source machine learning framework for Java and .Net. In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored. By the end of this training, participants will be able to: Prepare data for neural networks using the normalization process Implement feed forward networks and propagation training methodologies Implement classification and regression tasks Model and train neural networks using Encog's GUI based workbench Integrate neural network support into real-world applications Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
mlbankingr 机器学习用于银行业务(使用R) 28小时 在这一由讲师引导的现场培训中,参与者将学习如何应用机器学习技术和工具来解决银行业的现实问题。R将被用作编程语言。 参与者首先学习关键原则,然后通过建立自己的机器学习模型并使用模型来完成一些现场项目以将所学知识运用到实践中。 受众 开发人员 数据科学家 具有技术背景的银行专业人士 课程形式 部分讲座、部分讨论、练习和大量实操
sparkpython 用Spark和Python通过PySpark处理大数据 21小时 Spark是一个用于查询、分析和转换大数据的数据处理引擎。Python是一种高级编程语言,因其清晰的语法和代码可读性而闻名。PySpark允许用户将Spark与Python连接。 在这一由讲师引导的现场培训中,学员将通过实践练习学习如何使用Python和Spark一起分析大数据。 在本次培训结束后,学员将能够: 了解如何使用Spark和Python一起分析大数据 开展模拟真实世界环境的练习 用不同的工具和技术通过PySpark进行大数据分析 受众 开发人员 IT专业人士 数据科学家 课程形式 部分讲座、部分讨论、练习和大量实操
brmsdrools Business Rule Management (BRMS) with Drools 7小时 This course is aimed at enterprise architects, business and system analysts and managers who want to apply business rules to their solution. With Drools you can write your business rules using almost natural language, therefore reducing the gap between business and IT.
hadoopadm Hadoop Administration 21小时 The course is dedicated to IT specialists that are looking for a solution to store and process large data sets in distributed system environment Course goal: Getting knowledge regarding Hadoop cluster administration
annmldt Artificial Neural Networks, Machine Learning, Deep Thinking 21小时
sparkdev Spark for Developers 21小时 OBJECTIVE: This course will introduce Apache Spark. The students will learn how  Spark fits  into the Big Data ecosystem, and how to use Spark for data analysis.  The course covers Spark shell for interactive data analysis, Spark internals, Spark APIs, Spark SQL, Spark streaming, and machine learning and graphX. AUDIENCE : Developers / Data Analysts
caffe Deep Learning for Vision with Caffe 21小时 Caffe is a deep learning framework made with expression, speed, and modularity in mind. This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework. After completing this course, delegates will be able to: understand Caffe’s structure and deployment mechanisms carry out installation / production environment / architecture tasks and configuration assess code quality, perform debugging, monitoring implement advanced production like training models, implementing layers and logging
neo4j Beyond the relational database: neo4j 21小时 Relational, table-based databases such as Oracle and MySQL have long been the standard for organizing and storing data. However, the growing size and fluidity of data have made it difficult for these traditional systems to efficiently execute highly complex queries on the data. Imagine replacing rows-and-columns-based data storage with object-based data storage, whereby entities (e.g., a person) could be stored as data nodes, then easily queried on the basis of their vast, multi-linear relationship with other nodes. And imagine querying these connections and their associated objects and properties using a compact syntax, up to 20 times lighter than SQL. This is what graph databases, such as neo4j offer. In this hands-on course, we will set up a live project and put into practice the skills to model, manage and access your data. We contrast and compare graph databases with SQL-based databases as well as other NoSQL databases and clarify when and where it makes sense to implement each within your infrastructure. Audience Database administrators (DBAs) Data analysts Developers System Administrators DevOps engineers Business Analysts CTOs CIOs Format of the course Heavy emphasis on hands-on practice. Most of the concepts are learned through samples, exercises and hands-on development.
BigData_ 数据分析和大数据的实用介绍 35小时 参与者完成此次培训后,将会对大数据及其相关技术、方法、工具有一个实际和真实的理解。 参与者将有机会通过动手练习将这些知识付诸实践。小组互动和讲师反馈是课堂的重要组成部分。 本课程首先介绍大数据的基本概念,然后讲解用于执行数据分析的编程语言和方法,最后我们会讨论可启用大数据存储、分布式处理及可扩展性的工具和基础架构。 受众 开发人员/程序员 IT顾问 课程形式 部分讲座、部分讨论、实操、偶尔测评进度
matlabdsandreporting MATLAB基础、数据科学和报告生成 126小时 本次培训的第一部分介绍了MATLAB的基本原理及其作为语言和平台的功能。本次讨论包括MATLAB语法、数组和矩阵、数据可视化、脚本开发及面向对象原理的介绍。 在第二部分中,我们演示如何使用MATLAB进行数据挖掘、机器学习和预测性分析。为了给参与者一个关于MATLAB方法和功能的清晰和实用的观点,我们将使用MATLAB和使用电子表格、C、C ++、Visual Basic等其他工具进行比较。 在培训的第三部分,参与者学习如何通过自动化数据处理和报告生成来简化工作。 在整个课程中,参与者将在实验室环境里把通过动手练习学到的想法付诸实践。培训结束后,参与者将对MATLAB的功能有一个全面的掌握,并将能够用它来解决实际的数据科学问题,并通过自动化来简化他们的工作。 整个课程中将进行评估以衡量进度。 课程形式 课程包含理论和实践练习,包括案例讨论、样本代码检查和实操。 注意 练习课程将根据预先安排的样本数据报告模板进行。如果您有特殊要求,请联系我们以作安排。
mldlnlpintro ML、DL與NLP入門與進階大綱 14小时 The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
encogadv Encog: Advanced Machine Learning 14小时 Encog is an open-source machine learning framework for Java and .Net. In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models. By the end of this training, participants will be able to: Implement different neural networks optimization techniques to resolve underfitting and overfitting Understand and choose from a number of neural network architectures Implement supervised feed forward and feedback networks Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
mlbankingpython_ 机器学习用于银行业务(使用Python) 21小时 在这一由讲师引导的现场培训中,参与者将学习如何应用机器学习技术和工具来解决银行业的现实问题。Python将被用作编程语言。 参与者首先学习关键原则,然后通过建立自己的机器学习模型并使用模型来完成一些现场项目以将所学知识运用到实践中。 受众 开发人员 数据科学家 课程形式 部分讲座、部分讨论、练习和大量实操
mlfinancepython 机器学习用于金融领域(使用Python) 21小时 机器学习是人工智能的一个分支,指计算机可以在不被明确编程的情况下学习。 在这一由讲师引导的现场培训中,参与者将学习如何应用机器学习技术和工具来解决财务的现实问题。Python将被用作编程语言。 参与者首先学习关键原则,然后通过建立自己的机器学习模型并使用模型来完成一些团队项目以将所学知识运用到实践中。 在本次培训结束后,参与者将能够: 了解机器学习的基本概念 了解机器学习在金融领域的应用和使用 使用Python机器学习开发自己的算法交易策略 受众 开发人员 数据科学家 课程形式 部分讲座、部分讨论、练习和大量实操
aiint Artificial Intelligence Overview 7小时 This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and everyone who is interested overview of applied artificial intelligence and the nearest forecast for its development.
datamin Data Mining 21小时 Course can be provided with any tools, including free open-source data mining software and applications
dsguihtml5jsre Designing Inteligent User Interface with HTML5, JavaScript and Rule Engines 21小时 Coding interfaces which allow users to get what they want easily is hard. This course guides you how to create effective UI with newest technologies and libraries. It introduces idea of coding logic in Rule Engines (mostly Nools and PHP Rules) to make it easier to modify and test. After that the courses shows a way of integrating the logic on the front end of the website using JavaScript. Logic coded this way can be reused on the backend.
hbasedev HBase for Developers 21小时 This course introduces HBase – a NoSQL store on top of Hadoop.  The course is intended for developers who will be using HBase to develop applications,  and administrators who will manage HBase clusters. We will walk a developer through HBase architecture and data modelling and application development on HBase. It will also discuss using MapReduce with HBase, and some administration topics, related to performance optimization. The course  is very  hands-on with lots of lab exercises. Duration : 3 days Audience : Developers  & Administrators
68736 Hadoop for Developers (2 days) 14小时
datamodeling Pattern Recognition 35小时 This course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired. Audience     Data analysts     PhD students, researchers and practitioners  
octnp Octave not only for programmers 21小时 Course is dedicated for those who would like to know an alternative program to the commercial MATLAB package. The three-day training provides comprehensive information on moving around the environment and performing the OCTAVE package for data analysis and engineering calculations. The training recipients are beginners but also those who know the program and would like to systematize their knowledge and improve their skills. Knowledge of other programming languages is not required, but it will greatly facilitate the learners' acquisition of knowledge. The course will show you how to use the program in many practical examples.
tpuprogramming TPU Programming: Building Neural Network Applications on Tensor Processing Units 7小时 The Tensor Processing Unit (TPU) is the architecture which Google has used internally for several years, and is just now becoming available for use by the general public. It includes several optimizations specifically for use in neural networks, including streamlined matrix multiplication, and 8-bit integers instead of 16-bit in order to return appropriate levels of precision. In this instructor-led, live training, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications. By the end of the training, participants will be able to: Train various types of neural networks on large amounts of data Use TPUs to speed up the inference process by up to two orders of magnitude Utilize TPUs to process intensive applications such as image search, cloud vision and photos Audience Developers Researchers Engineers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
jupyter Jupyter for Data Science Teams 7小时 Jupyter is an open-source, web-based interactive IDE and computing environment. This instructor-led, live training introduces the idea of collaborative development in data science and demonstrates how to use Jupyter to track and participate as a team in the "life cycle of a computational idea".  It walks participants through the creation of a sample data science project based on top of the Jupyter ecosystem. By the end of this training, participants will be able to: Install and configure Jupyter, including the creation and integration of a team repository on Git Use Jupyter features such as extensions, interactive widgets, multiuser mode and more to enable project collaboraton Create, share and organize Jupyter Notebooks with team members Choose from Scala, Python, R, to write and execute code against big data systems such as Apache Spark, all through the Jupyter interface Audience Data science teams Format of the course Part lecture, part discussion, exercises and heavy hands-on practice   Note The Jupypter Notebook supports over 40 languages including R, Python, Scala, Julia, etc. To customize this course to your language(s) of choice, please contact us to arrange.
pythontextml Python:用文本进行机器学习 21小时 在这一由讲师引导的现场培训中,参与者将学习如何使用正确的机器学习和NLP(自然语言处理)技术从基于文本的数据中提取价值。 在本次培训结束后,参与者将能够: 用高质量、可重用的代码解决基于文本的数据科学问题 运用scikit-learn的不同方面(分类、聚类、回归、降维)来解决问题 使用基于文本的数据建立有效的机器学习模型 创建一个数据集并从非结构化文本中提取特征 用Matplotlib可视化数据 构建和评估模型以获得洞察力 解决文本编码错误 受众 开发人员 数据科学家 课程形式 部分讲座、部分讨论、练习和大量实操
opennlp OpenNLP for Text Based Machine Learning 14小时 The Apache OpenNLP library is a machine learning based toolkit for processing natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution. In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises. By the end of this training, participants will be able to: Install and configure OpenNLP Download existing models as well as create their own Train the models on various sets of sample data Integrate OpenNLP with existing Java applications Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
pythoncomputervision Computer Vision with Python 7小时 Computer Vision is a field that involves automatically extracting, analyzing, and understanding useful information from digital media. Python is a high-level programming language famous for its clear syntax and code readibility. In this instructor-led, live training, participants will learn the basics of Computer Vision as they step through the creation of simple Computer Vision apps using Python. By the end of this training, participants will be able to: Understand the basics of Computer Vision Use Python to implement Computer Vision tasks Build their own Computer Vision apps using Python Audience Python programmers interested in Computer Vision Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
smtwebint Semantic Web Overview 7小时 The Semantic Web is a collaborative movement led by the World Wide Web Consortium (W3C) that promotes common formats for data on the World Wide Web. The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries.
droolsrlsadm Drools Rules Administration 21小时 This course has been prepared for people who are involved in administering corporate knowledge assets (rules, process) like system administrators, system integrators, application server administrators, etc... We are using the newest stable community version of Drools to run this course, but older versions are also possible if agreed before booking.
datashrinkgov Data Shrinkage for Government 14小时
droolsdslba Drools 6 and DSL for Business Analysts 21小时 This 3 days course is aimed to introduce Drools 6 to Business Analysts responsible for writing tests and rules. This course focuses on creating pure logic. Analysts after this course can writing tests and logic which then can be further integrated by developers with business applications.
datavis1 Data Visualization 28小时 This course is intended for engineers and decision makers working in data mining and knoweldge discovery. You will learn how to create effective plots and ways to present and represent your data in a way that will appeal to the decision makers and help them to understand hidden information.
processmining Process Mining 21小时 Process mining, or Automated Business Process Discovery (ABPD), is a technique that applies algorithms to event logs for the purpose of analyzing business processes. Process mining goes beyond data storage and data analysis; it bridges data with processes and provides insights into the trends and patterns that affect process efficiency.  Format of the course     The course starts with an overview of the most commonly used techniques for process mining. We discuss the various process discovery algorithms and tools used for discovering and modeling processes based on raw event data. Real-life case studies are examined and data sets are analyzed using the ProM open-source framework. Audience     Data science professionals     Anyone interested in understanding and applying process modeling and data mining
mlentre Machine Learning Concepts for Entrepreneurs and Managers 21小时 This training course is for people that would like to apply Machine Learning in practical applications for their team.  The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same. Target Audience Investors and AI entrepreneurs Managers and Engineers whose company is venturing into AI space Business Analysts & Investors
TalendDI Talend Open Studio for Data Integration 28小时 Talend Open Studio for Data Integration is an open-source data integration product used to combine, convert and update data in various locations across a business. In this instructor-led, live training, participants will learn how to use the Talend ETL tool to carry out data transformation, data extraction, and connectivity with Hadoop, Hive, and Pig.   By the end of this training, participants will be able to Explain the concepts behind ETL (Extract, Transform, Load) and propagation Define ETL methods and ETL tools to connect with Hadoop Efficiently amass, retrieve, digest, consume, transform and shape big data in accordance to business requirements Upload to and extract large records from Hadoop, Hive, and NoSQL databases Audience Business intelligence professionals Project managers Database professionals SQL Developers ETL Developers Solution architects Data architects Data warehousing professionals System administrators and integrators Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
datameer Datameer for Data Analysts 14小时 Datameer is a business intelligence and analytics platform built on Hadoop. It allows end-users to access, explore and correlate large-scale, structured, semi-structured and unstructured data in an easy-to-use fashion. In this instructor-led, live training, participants will learn how to use Datameer to overcome Hadoop's steep learning curve as they step through the setup and analysis of a series of big data sources. By the end of this training, participants will be able to: Create, curate, and interactively explore an enterprise data lake Access business intelligence data warehouses, transactional databases and other analytic stores Use a spreadsheet user-interface to design end-to-end data processing pipelines Access pre-built functions to explore complex data relationships Use drag-and-drop wizards to visualize data and create dashboards Use tables, charts, graphs, and maps to analyze query results Audience Data analysts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
flockdb Flockdb: A Simple Graph Database for Social Media 7小时 FlockDB is an open source distributed, fault-tolerant graph database for managing wide but shallow network graphs. It was initially used by Twitter to store relationships among users. In this instructor-led, live training, participants will learn how to setup and use a FlockDB database to help answer social media questions such as who follows whom, who blocks whom, etc. By the end of this training, participants will be able to: Install and configure FlockDB Understand the unique features of FlockDB, relative to other graph databases such Neo4j Use FlockDB to maintain a large graph dataset Use FlockDB together with MySQL to provide provide distributed storage capabilities Query, create and update extremely fast graph edges Scale FlockDB horizontally for use in on-line, low-latency, high throughput web environments Audience Developers Database engineers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
undnn Understanding Deep Neural Networks 35小时 This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications). Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc. Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy. Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow. Audience This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects After completing this course, delegates will: have a good understanding on deep neural networks(DNN), CNN and RNN understand TensorFlow’s structure and deployment mechanisms be able to carry out installation / production environment / architecture tasks and configuration be able to assess code quality, perform debugging, monitoring be able to implement advanced production like training models, building graphs and logging   Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject. The Duration of the complete course will be around 70 hours and not 35 hours.
mlfinancer Machine Learning for Finance (with R) 28小时 Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. By the end of this training, participants will be able to: Understand the fundamental concepts in machine learning Learn the applications and uses of machine learning in finance Develop their own algorithmic trading strategy using machine learning with R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
d2dbdpa From Data to Decision with Big Data and Predictive Analytics 21小时 Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.
matlab2 MATLAB 基础 21小时 MATLAB软件简介 MATLAB(矩阵实验室)是MATrix LABoratory的缩写,是一款由美国The MathWorks公司出品的商业科学计算和仿真软件.MATLAB拥有一套可用于算法开发,数据可视化,数据分析以及数值计算的高级技术计算语言和交互式环境.除了矩阵运算,求解线性系统方程,绘制函数/数据图像等常用功能外,MATLAB还可以用来创建用户界面及与调用其它语言(包括C,C++,Java,Python和FORTRAN)编写的程序。 尽管MATLAB最初主要用于科学计算,但其不断增加的各种附加工具箱(到目前为止将近100个)使之适合不同领域和行业的应用,如控制系统设计与分析,生物医疗,图像处理,信号处理与通讯,金融建模和分析,汽车,航天航空等。另外还有一个基于模型化设计(MBD)的图形化仿真软件包Simulink用于系统模拟,代码生成,动态/嵌入式系统开发等方面. 培训目的  本课程将全面介绍MATLAB科学技术计算环境,旨在于使初学者迅速掌握MATLAB原理,在课程结束后可以: -> 熟悉MATLAB界面,查找帮助; -> 键入命令,进行变量,向量和矩阵的基本操作; -> 对数据进行多种可视化展示; -> 处理数据文件和不同数据类型; -> 编写脚本和函数,并在其中包含必要的逻辑和分支控制; -> 读写文本和二进制文件 课程特色 本次课程使用MATLAB2014a用于演示。本着由浅入深,注重实践,重点问题反复强调的原则,不拘泥于PPT讲义,尽量多使用实例进行示范操作.
python_nltk Natural Language Processing with Python 28小时 This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking.
genealgo Genetic Algorithms 28小时 This four day course is aimed at teaching how genetic algorithms work; it also covers how to select model parameters of a genetic algorithm; there are many applications for genetic algorithms in this course and optimization problems are tackled with the genetic algorithms.
opencv Computer Vision with OpenCV 28小时 OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms. Audience This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects
teraintro Teradata Fundamentals 21小时 Teradata is one of the popular Relational Database Management System. It is mainly suitable for building large scale data warehousing applications. Teradata achieves this by the concept of parallelism.  This course introduces the delegates to Teradata
pythonmultipurpose 高级Python 28小时 在这一由讲师引导的培训中,参与者将学习高级Python编程技术,包括如何将这种多功能语言应用于解决分布式应用、财务、数据分析和可视化、UI编程及维护脚本等领域的问题。 受众 开发人员 课程形式 部分讲座、部分讨论、练习和大量实操 注意事项 如果您想添加、移除或自定义本课程中的任一部分或主题,请联系我们以作安排。
dsstne Amazon DSSTNE: Build a recommendation system 7小时 Amazon DSSTNE is an open-source library for training and deploying recommendation models. It allows models with weight matrices that are too large for a single GPU to be trained on a single host. In this instructor-led, live training, participants will learn how to use DSSTNE to build a recommendation application. By the end of this training, participants will be able to: Train a recommendation model with sparse datasets as input Scale training and prediction models over multiple GPUs Spread out computation and storage in a model-parallel fashion Generate Amazon-like personalized product recommendations Deploy a production-ready application that can scale at heavy workloads Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
datavisualizationreports Data Visualization: Creating Captivating Reports 21小时 In this instructor-led, live training, participants will learn the skills, strategies, tools and approaches for visualizing and reporting data for different audiences. Case studies are also analyzed and discussed to exemplify how data visualization solutions are being applied in the real world to derive meaning out of data and answer crucial questions. By the end of this training, participants will be able to: Write reports with captivating titles, subtitles, and annotations using the most suitable highlighting, alignment, and color schemes for readability and user friendliness. Design charts that fit the audience's information needs and interests Choose the best chart types for a given dataset (beyond pie charts and bar charts) Identify and analyze the most valuable and relevant data quickly and efficiently Select the best file formats to include in reports (graphs, infographics, references, GIFs, etc.) Create effective layouts for displaying time series data, part-to-whole relationships, geographic patterns, and nested data Use effective color-coding to display qualitative and text-based data such as sentiment analysis, timelines, calendars, and diagrams Apply the most suitable tools for the job (Excel, R, Tableau, mapping programs, etc.) Prepare datasets for visualization Audience Data analysts Business managers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
d3js D3.js for Data Visualization 7小时 D3.js (or D3 for Data-Driven Documents) is a JavaScript library that uses SVG, HTML5, and CSS for producing dynamic, interactive data visualizations in web browsers. In this instructor-led, live training, participants will learn how to create web-based data-driven visualizations that run on multiple devices responsively. By the end of this training, participants will be able to: Use D3 to create interactive graphics, information dashboards, infographics and maps Control HTML with jQuery-like selections Transform the DOM by selecting elements and joining to data Export SVG for use in print publications Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
dlfinancewithr Deep Learning for Finance (with R) 28小时 Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in finance Use R to create deep learning models for finance Build their own deep learning stock price prediction model using R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
mdlmrah Model MapReduce and Apache Hadoop 14小时 The course is intended for IT specialist that works with the distributed processing of large data sets across clusters of computers.
optaprac OptaPlanner in Practice 21小时 This course uses a practical approach to teaching OptaPlanner. It provides participants with the tools needed to perform the basic functions of this tool.
mlrobot1 Machine Learning for Robotics 21小时 This course introduce machine learning methods in robotics applications. It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition. After short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software.
mlfsas Machine Learning Fundamentals with Scala and Apache Spark 14小时 The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
datavisR1 Introduction to Data Visualization with R 28小时 This course is intended for data engineers, decision makers and data analysts and will lead you to create very effective plots using R studio that appeal to decision makers and help them find out hidden information and take the right decisions  
patternmatching Pattern Matching 14小时 Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not. Audience     Engineers and developers seeking to develop machine vision applications     Manufacturing engineers, technicians and managers Format of the course     This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
opennmt OpenNMT: Setting up a Neural Machine Translation System 7小时 OpenNMT is a full-featured, open-source (MIT) neural machine translation system that utilizes the Torch mathematical toolkit. In this training participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor. By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution. Source and target language samples will be pre-arranged per the audience's requirements. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice
MicrosoftCognitiveToolkit Microsoft Cognitive Toolkit 2.x 21小时 Microsoft Cognitive Toolkit 2.x (previously CNTK) is an open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. According to Microsoft, CNTK can be 5-10x faster than TensorFlow on recurrent networks, and 2 to 3 times faster than TensorFlow for image-related tasks. In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such data, speech, text, and images. By the end of this training, participants will be able to: Access CNTK as a library from within a Python, C#, or C++ program Use CNTK as a standalone machine learning tool through its own model description language (BrainScript) Use the CNTK model evaluation functionality from a Java program Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs) Scale computation capacity on CPUs, GPUs and multiple machines Access massive datasets using existing programming languages and algorithms Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange.
cognitivecomputing Cognitive Computing: An Introduction for Business Managers 7小时 Cognitive computing refers to systems that encompass machine learning, reasoning, natural language processing, speech recognition and vision (object recognition), human–computer interaction, dialog and narrative generation, to name a few. A cognitive computing system is often comprised of multiple technologies working together to process in-memory ‘hot’ contextual data as well as large sets of ‘cold’ historical data in batch. Examples of such technologies include Kafka, Spark, Elasticsearch, Cassandra and Hadoop. In this instructor-led, live training, participants will learn how Cognitive Computing compliments AI and Big Data and how purpose-built systems can be used to realize human-like behaviors that improve the performance of human-machine interactions in business. By the end of this training, participants will understand: The relationship between cognitive computing and artificial intelligence (AI) The inherently probabilistic nature of cognitive computing and how to use it as a business advantage How to manage cognitive computing systems that behave in unexpected ways Which companies and software systems offer the most compelling cognitive computing solutions Audience Business managers Format of the course Lecture, case discussions and exercises
nlg Python for Natural Language Generation 21小时 Natural language generation (NLG) refers to the production of natural language text or speech by a computer. In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content. By the end of this training, participants will be able to: Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting Select and organize source content, plan sentences, and prepare a system for automatic generation of original content Understand the NLG pipeline and apply the right techniques at each stage Understand the architecture of a Natural Language Generation (NLG) system Implement the most suitable algorithms and models for analysis and ordering Pull data from publicly available data sources as well as curated databases to use as material for generated text Replace manual and laborious writing processes with computer-generated, automated content creation Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
highcharts Highcharts for Data Visualization 7小时 Highcharts is an open-source JavaScript library for creating interactive graphical charts on the Web. It is commonly used to represent data in a more user-readable and interactive fashion. In this instructor-led, live training, participants will learn how to create high-quality data visualizations for web applications using Highcharts. By the end of this training, participants will be able to: Set up interactive charts on the Web using only HTML and JavaScript Represent large datasets in visually interesting and interactive ways Export charts to JPEG, PNG, SVG, or PDF Integrate Highcharts with jQuery Mobile for cross-platform compatibility Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
dlforbankingwithpython Deep Learning for Banking (with Python) 28小时 Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in banking Use Python, Keras, and TensorFlow to create deep learning models for banking Build their own deep learning credit risk model using Python Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
nlp Natural Language Processing 21小时 This course has been designed for people interested in extracting meaning from written English text, though the knowledge can be applied to other human languages as well. The course will cover how to make use of text written by humans, such as  blog posts, tweets, etc... For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source.
mlintro Introduction to Machine Learning 7小时 This training course is for people that would like to apply basic Machine Learning techniques in practical applications. Audience Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work Sector specific examples are used to make the training relevant to the audience.
deeplearning1 Introduction to Deep Learning 21小时 This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.
tf101 Deep Learning with TensorFlow 21小时 TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system. Audience This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects After completing this course, delegates will: understand TensorFlow’s structure and deployment mechanisms be able to carry out installation / production environment / architecture tasks and configuration be able to assess code quality, perform debugging, monitoring be able to implement advanced production like training models, building graphs and logging
dlv Deep Learning for Vision 21小时 Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source ) for analyzing computer images This course provide working examples.
kdd Knowledge Discover in Databases (KDD) 21小时 Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing. In this course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes. Audience     Data analysts or anyone interested in learning how to interpret data to solve problems Format of the course     After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations.
Fairsec Fairsec: Setting up a CNN-based machine translation system 7小时 Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Source and target language content samples can be prepared according to audience's requirements. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course     Part lecture, part discussion, heavy hands-on practice
PentahoDI Pentaho Data Integration Fundamentals 21小时 Pentaho Data Integration is an open-source data integration tool for defining jobs and data transformations. In this instructor-led, live training, participants will learn how to use Pentaho Data Integration's powerful ETL capabilities and rich GUI to manage an entire big data lifecycle, maximizing the value of data to the organization. By the end of this training, participants will be able to: Create, preview, and run basic data transformations containing steps and hops Configure and secure the Pentaho Enterprise Repository Harness disparate sources of data and generate a single, unified version of the truth in an analytics-ready format. Provide results to third-part applications for further processing Audience Data Analyst ETL developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
datavault Data Vault: Building a Scalable Data Warehouse 28小时 Data vault modeling is a database modeling technique that provides long-term historical storage of data that originates from multiple sources. A data vault stores a single version of the facts, or "all the data, all of the time". Its flexible, scalable, consistent and adaptable design encompasses the best aspects of 3rd normal form (3NF) and star schema. In this instructor-led, live training, participants will learn how to build a Data Vault. By the end of this training, participants will be able to: Understand the architecture and design concepts behind Data Vault 2.0, and its interaction with Big Data, NoSQL and AI. Use data vaulting techniques to enable auditing, tracing, and inspection of historical data in a data warehouse Develop a consistent and repeatable ETL (Extract, Transform, Load) process Build and deploy highly scalable and repeatable warehouses Audience Data modelers Data warehousing specialist Business Intelligence specialists Data engineers Database administrators Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
odmblockchain IBM ODM and Blockchain: Applying business rules to Smart Contracts 14小时 Smart Contracts are used to encode and encapsulate the rules for automatically initiating and processing transactions on the Blockchain. In this instructor-led, live training, participants will learn how to use IBM Operational Decision Manager (ODM) with Hyperledger Composer to implement the business logic of a Smart Contract using business rules. By the end of this training, participants will be able to: Use ODM's rule engine together with Blockchain to "unbury" rules from the codebase of a Blockchain application Set up a system to allow specialist such as accountants, auditors, lawyers, and analysts to define the rules of exchange for themselves Use Decision Center as a platform to collaboratively govern rules Use ODM's rule engine to update, test and deploy rules without touching the code of the Smart Contract Deploy the IBM ODM Rule Execution Server Integrate IBM ODM with Hyperledger Composer running on Hyperledger Fabric Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
python_nlp Natural Language Processing with Deep Dive in Python and NLTK 35小时 By the end of the training the delegates are expected to be sufficiently equipped with the essential python concepts and should be able to sufficiently use NLTK to implement most of the NLP and ML based operations. The training is aimed at giving not just an executional knowledge but also the logical and operational knowledge of the technology therein.  
dlforbankingwithr Deep Learning for Banking (with R) 28小时 Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in banking Use R to create deep learning models for banking Build their own deep learning credit risk model using R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
wfsadm WildFly Server Administration 14小时 This course is created for Administrators, Developers or anyone who is interested in managing WildFly Application Server (AKA JBoss Application Server). This course usually runs on the newest version of the Application Server, but it can be tailored (as a private course) to older versions starting from version 5.1.
appliedml Applied Machine Learning 14小时 This training course is for people that would like to apply Machine Learning in practical applications. Audience This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience.
matlabml1 MATLAB与机器学习入门 21小时 MATLAB is a numerical computing environment and programming language developed by MathWorks.
predio Machine Learning with PredictionIO 21小时 PredictionIO is an open source Machine Learning Server built on top of state-of-the-art open source stack. Audience This course is directed at developers and data scientists who want to create predictive engines for any machine learning task.
aiauto Artificial Intelligence in Automotive 14小时 This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.
scylladb Scylla database 21小时 Scylla is an open-source distributed NoSQL data store. It is compatible with Apache Cassandra but performs at significantly higher throughputs and lower latencies. In this course, participants will learn about Scylla's features and architecture while obtaining practical experience with setting up, administering, monitoring, and troubleshooting Scylla.   Audience     Database administrators     Developers     System Engineers Format of the course     The course is interactive and includes discussions of the principles and approaches for deploying and managing Scylla distributed databases and clusters. The course includes a heavy component of hands-on exercises and practice.
ApHadm1 Apache Hadoop: Manipulation and Transformation of Data Performance 21小时 This course is intended for developers, architects, data scientists or any profile that requires access to data either intensively or on a regular basis. The major focus of the course is data manipulation and transformation. Among the tools in the Hadoop ecosystem this course includes the use of Pig and Hive both of which are heavily used for data transformation and manipulation. This training also addresses performance metrics and performance optimisation. The course is entirely hands on and is punctuated by presentations of the theoretical aspects.
hdp Hortonworks Data Platform (HDP) for administrators 21小时 Hortonworks Data Platform is an open-source Apache Hadoop support platform that provides a stable foundation for developing big data solutions on the Apache Hadoop ecosystem. This instructor-led live training introduces Hortonworks and walks participants through the deployment of Spark + Hadoop solution. By the end of this training, participants will be able to: Use Hortonworks to reliably run Hadoop at a large scale Unify Hadoop's security, governance, and operations capabilities with Spark's agile analytic workflows. Use Hortonworks to investigate, validate, certify and support each of the components in a Spark project Process different types of data, including structured, unstructured, in-motion, and at-rest. Audience Hadoop administrators Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
t2t T2T: Creating Sequence to Sequence models for generalized learning 7小时 Tensor2Tensor (T2T) is a modular, extensible library for training AI models in different tasks, using different types of training data, for example: image recognition, translation, parsing, image captioning, and speech recognition. It is maintained by the Google Brain team. In this instructor-led, live training, participants will learn how to prepare a deep-learning model to resolve multiple tasks. By the end of this training, participants will be able to: Install tensor2tensor, select a data set, and train and evaluate an AI model Customize a development environment using the tools and components included in Tensor2Tensor Create and use a single model to concurrently learn a number of tasks from multiple domains Use the model to learn from tasks with a large amount of training data and apply that knowledge to tasks where data is limited Obtain satisfactory processing results using a single GPU Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
fsharpfordatascience F# for Data Science 21小时 Data science is the application of statistical analysis, machine learning, data visualization and programming for the purpose of understanding and interpreting real-world data. F# is a well suited programming language for data science as it combines efficient execution, REPL-scripting, powerful libraries and scalable data integration. In this instructor-led, live training, participants will learn how to use F# to solve a series of real-world data science problems. By the end of this training, participants will be able to: Use F#'s integrated data science packages Use F# to interoperate with other languages and platforms, including Excel, R, Matlab, and Python Use the Deedle package to solve time series problems Carry out advanced analysis with minimal lines of production-quality code Understand how functional programming is a natural fit for scientific and big data computations Access and visualize data with F# Apply F# for machine learning Explore solutions for problems in domains such as business intelligence and social gaming Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
graphcomputing Introduction to Graph Computing 28小时 A large number of real world problems can be described in terms of graphs. For example, the Web graph, the social network graph, the train network graph and the language graph. These graphs tend to be extremely large; processing them requires a specialized set of tools and mindset referred to as graph computing. In this instructor-led, live training, participants will learn about the various technology offerings and implementations for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using graph computing approaches. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments. By the end of this training, participants will be able to: Understand how graph data is persisted and traversed Select the best framework for a given task (from graph databases to batch processing frameworks) Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel View real-world big data problems in terms of graphs, processes and traversals Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
dlforfinancewithpython Deep Learning for Finance (with Python) 28小时 Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in finance Use Python, Keras, and TensorFlow to create deep learning models for finance Build their own deep learning stock price prediction model using Python Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
drools6int Introduction to Drools 6 for Developers 21小时 This 3 days course is aimed to introduce Drools 6 to developers.This course doesn't cover drools integration, performance or any other complex topics.
apachemdev Apache Mahout for Developers 14小时 Audience Developers involved in projects that use machine learning with Apache Mahout. Format Hands on introduction to machine learning. The course is delivered in a lab format based on real world practical use cases.
dladv Advanced Deep Learning 28小时
cntk Using Computer Network ToolKit (CNTK) 28小时 Computer Network ToolKit (CNTK) is Microsoft's Open Source, Multi-machine, Multi-GPU, Highly efficent RNN training machine learning framework for speech, text, and images. Audience This course is directed at engineers and architects aiming to utilize CNTK in their projects.
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example 28小时 This course will give you knowledge in neural networks and generally in machine learning algorithm,  deep learning (algorithms and applications). This training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
marvin Marvin Image Processing Framework - creating image and video processing applications with Marvin 14小时 Marvin is an extensible, cross-platform, open-source image and video processing framework developed in Java.  Developers can use Marvin to manipulate images, extract features from images for classification tasks, generate figures algorithmically, process video file datasets, and set up unit test automation. Some of Marvin's video applications include filtering, augmented reality, object tracking and motion detection. In this course participants will learn the principles of image and video analysis and utilize the Marvin Framework and its image processing algorithms to construct their own application. Audience     Software developers wishing to utilize a rich, plug-in based open-source framework to create image and video processing applications Format of the course     The basic principles of image analysis, video analysis and the Marvin Framework are first introduced. Students are given project-based tasks which allow them to practice the concepts learned. By the end of the class, participants will have developed their own application using the Marvin Framework and libraries.
cpb100 Google Cloud Platform Fundamentals: Big Data & Machine Learning 8小时 This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform. This course teaches participants the following skills: Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform. Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform. Employ BigQuery and Cloud Datalab to carry out interactive data analysis. Train and use a neural network using TensorFlow. Employ ML APIs. Choose between different data processing products on the Google Cloud Platform. This class is intended for the following: Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform. Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports. Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists.
magellan Magellan: Geospatial Analytics with on Spark 14小时 Magellan is an open-source distributed execution engine for geospatial analytics on big data. Implemented on top of Apache Spark, it extends Spark SQL and provides a relational abstraction for geospatial analytics. This instructor-led, live training introduces the concepts and approaches for implementing geospacial analytics and walks participants through the creation of a predictive analysis application using Magellan on Spark. By the end of this training, participants will be able to: Efficiently query, parse and join geospatial datasets at scale Implement geospatial data in business intelligence and predictive analytics applications Use spatial context to extend the capabilities of mobile devices, sensors, logs, and wearables Audience Application developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
deckgl deck.gl: Visualizing Large-scale Geospatial Data 14小时 deck.gl is an open-source, WebGL-powered library for exploring and visualizing data assets at scale. Created by Uber, it is especially useful for gaining insights from geospatial data sources, such as data on maps. This instructor-led, live training introduces the concepts and functionality behind deck.gl and walks participants through the set up of a demonstration project. By the end of this training, participants will be able to: Take data from very large collections and turn it into compelling visual representations Visualize data collected from transportation and journey-related use cases, such as pick-up and drop-off experiences, network traffic, etc. Apply layering techniques to geospatial data to depict changes in data over time Integrate deck.gl with React (for Reactive programming) and Mapbox GL (for visualizations on Mapbox based maps). Understand and explore other use cases for deck.gl, including visualizing points collected from a 3D indoor scan, visualizing machine learning models in order to optimize their algorithms, etc. Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
mlios Machine Learning on iOS 14小时 In this instructor-led, live training, participants will learn how to use the iOS Machine Learning (ML) technology stack as they as they step through the creation and deployment of an iOS mobile app. By the end of this training, participants will be able to: Create a mobile app capable of image processing, text analysis and speech recognition Access pre-trained ML models for integration into iOS apps Create a custom ML model Add Siri Voice support to iOS apps Understand and use frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit Use languages and tools such as Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
pythonfinance Python用于财务工作 35小时 Python是一门在金融行业拥有巨大声望的编程语言。最大的投资银行和对冲基金正在使用它来构建包括核心交易项目及风险管理系统在内的广泛的金融应用。 在这一由讲师引导的现场培训中,参与者将学习如何使用Python开发实际的应用程序以解决一些特定的财务相关的问题。 在本次培训结束后,参与者将能够: 了解Python编程语言的基础知识 下载、安装和维护用Python创建财务应用程序的最佳开发工具 选择和利用最合适的Python软件包和编程技术来组织、可视化和分析从各种来源(CSV、Excel、数据库、网站等)得来的财务数据。 构建解决资产配置、风险分析、投资绩效等相关问题的应用程序 故障排除、集成部署和优化他们的应用程序 受众 开发人员 分析师 宽客 课程形式 部分讲座、部分讨论、练习和大量实操 注意事项 该培训旨在为金融专业人士所面对的一些原则问题提供解决方案。但是,如果您有一个特定的主题、工具或技术想要附加或详细说明,请联系我们以作安排。
monetdb MonetDB 28小时 MonetDB is an open-source database that pioneered the column-store technology approach. In this instructor-led, live training, participants will learn how to use MonetDB and how to get the most value out of it. By the end of this training, participants will be able to: Understand MonetDB and its features Install and get started with MonetDB Explore and perform different functions and tasks in MonetDB Accelerate the delivery of their project by maximizing MonetDB capabilities Audience Developers Technical experts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
noolsint Introduction to Nools 7小时
MLFWR1 Machine Learning Fundamentals with R 14小时 The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
bigdatastore Big Data Storage Solution - NoSQL 14小时 When traditional storage technologies don't handle the amount of data you need to store there are hundereds of alternatives. This course try to guide the participants what are alternatives for storing and analyzing Big Data and what are theirs pros and cons. This course is mostly focused on discussion and presentation of solutions, though hands-on exercises are available on demand.
systemml Apache SystemML for Machine Learning 14小时 Apache SystemML is a distributed and declarative machine learning platform. SystemML provides declarative large-scale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations, to distributed computations on Apache Hadoop and Apache Spark. Audience This course is suitable for Machine Learning researchers, developers and engineers seeking to utilize SystemML as a framework for machine learning.
cassadmin Cassandra Administration 14小时 This course will introduce Cassandra –  a popular NoSQL database.  It will cover Cassandra principles, architecture and data model.   Students will learn data modeling  in CQL (Cassandra Query Language) in hands-on, interactive labs.  This session also discusses Cassandra internals and some admin topics.
Torch Torch: Getting started with Machine and Deep Learning 21小时 Torch is an open source machine learning library and a scientific computing framework based on the Lua programming language. It provides a development environment for numerics, machine learning, and computer vision, with a particular emphasis on deep learning and convolutional nets. It is one of the fastest and most flexible frameworks for Machine and Deep Learning and is used by companies such as Facebook, Google, Twitter, NVIDIA, AMD, Intel, and many others. In this course we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned. By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects. Audience     Software developers and programmers wishing to enable Machine and Deep Learning within their applications Format of the course     Overview of Machine and Deep Learning     In-class coding and integration exercises     Test questions sprinkled along the way to check understanding
cpde Data Engineering on Google Cloud Platform 32小时 This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data. This course teaches participants the following skills: Design and build data processing systems on Google Cloud Platform Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow Derive business insights from extremely large datasets using Google BigQuery Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML Leverage unstructured data using Spark and ML APIs on Cloud Dataproc Enable instant insights from streaming data This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, Loading, Transforming, cleaning, and validating data Designing pipelines and architectures for data processing Creating and maintaining machine learning and statistical models Querying datasets, visualizing query results and creating reports
zeppelin Zeppelin for interactive data analytics 14小时 Apache Zeppelin is a web-based notebook for capturing, exploring, visualizing and sharing Hadoop and Spark based data. This instructor-led, live training introduces the concepts behind interactive data analytics and walks participants through the deployment and usage of Zeppelin in a single-user or multi-user environment. By the end of this training, participants will be able to: Install and configure Zeppelin Develop, organize, execute and share data in a browser-based interface Visualize results without referring to the command line or cluster details Execute and collaborate on long workflows Work with any of a number of plug-in language/data-processing-backends, such as Scala ( with Apache Spark ), Python ( with Apache Spark ), Spark SQL, JDBC, Markdown and Shell. Integrate Zeppelin with Spark, Flink and Map Reduce Secure multi-user instances of Zeppelin with Apache Shiro Audience Data engineers Data analysts Data scientists Software developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
embeddingprojector Embedding Projector: Visualizing your Training Data 14小时 Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow. This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project. By the end of this training, participants will be able to: Explore how data is being interpreted by machine learning models Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals. Explore the properties of a specific embedding to understand the behavior of a model Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
tensorflowserving TensorFlow Serving 7小时 TensorFlow Serving is a system for serving machine learning (ML) models to production. In this instructor-led, live training, participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment. By the end of this training, participants will be able to: Train, export and serve various TensorFlow models Test and deploy algorithms using a single architecture and set of APIs Extend TensorFlow Serving to serve other types of models beyond TensorFlow models Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
rforfinance R Programming for Finance 28小时 R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems. By the end of this training, participants will be able to: Understand the fundamentals of the R programming language Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.) Build applications that solve problems related to asset allocation, risk analysis, investment performance and more Troubleshoot, integrate deploy and optimize an R application Audience Developers Analysts Quants Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.
apachedrill Apache Drill用于飞速分析多种大数据格式 21小时 Apache Drill是一种无模式、分布式、内存列式SQL查询引擎,用于Hadoop、NoSQL及其他云和文件存储系统。Apache Drill的强大之处在于它能够使用单个查询连接来自多个数据存储的数据。Apache Drill支持许多NoSQL数据库和文件系统,包括HBase、MongoDB、MapR-DB、HDFS、MapR-FS、Amazon S3、Azure Blob Storage、Google Cloud Storage、Swift、NAS和本地文件。 在这一由讲师引导的现场培训中,学员将学习Apache Drill的基础知识,然后利用SQL的强大功能和便利性在无需编写代码的情况下交互式查询大数据。学员还将学习如何优化分布式SQL执行的Drill查询。 在本次培训结束后,学员将能够: 对Hadoop上的结构化和半结构化数据进行“自助式”探索 使用SQL查询来查询已知以及未知数据 了解Apache Drills如何接收和执行查询 编写SQL查询来分析不同类型的数据,包括Hive中的结构化数据,HBase或MapR-DB表中的半结构化数据,以及Parquet和JSON文件中保存的数据。 使用Apache Drill执行即时模式发现,绕过对复杂ETL和模式操作的需求 将Apache Drill与BI(商业智能)工具(如Tableau、Qlikview、MicroStrategy、Excel)集成在一起 受众 数据分析师 数据科学家 SQL程序员 课程形式 部分讲座、部分讨论、练习和大量实操
neuralnet Introduction to the use of neural networks 7小时 The training is aimed at people who want to learn the basics of neural networks and their applications.
mlfunpython Machine Learning Fundamentals with Python 14小时 The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
hadoopdeva Advanced Hadoop for Developers 21小时 Apache Hadoop is one of the most popular frameworks for processing Big Data on clusters of servers. This course delves into data management in HDFS, advanced Pig, Hive, and HBase.  These advanced programming techniques will be beneficial to experienced Hadoop developers. Audience: developers Duration: three days Format: lectures (50%) and hands-on labs (50%).  
tfir TensorFlow for Image Recognition 28小时 This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition Audience This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition After completing this course, delegates will be able to: understand TensorFlow’s structure and deployment mechanisms carry out installation / production environment / architecture tasks and configuration assess code quality, perform debugging, monitoring implement advanced production like training models, building graphs and logging
bpmndmncmmn BPMN, DMN, and CMNN - OMG standards for process improvement 28小时 Business Process Model and Notation (BPMN), Decision Model and Notation (DMN) and Case Management Model and Notation (CMMN) are three Object Management Group (OMG) standards for processes, decisions, and case modelling. This course provides an introduction to all of them and informs when should we use which.
OpenNN OpenNN: Implementing neural networks 14小时 OpenNN is an open-source class library written in C++  which implements neural networks, for use in machine learning. In this course we go over the principles of neural networks and use OpenNN to implement a sample application. Audience     Software developers and programmers wishing to create Deep Learning applications. Format of the course     Lecture and discussion coupled with hands-on exercises.
hadoopforprojectmgrs Hadoop for Project Managers 14小时 As more and more software and IT projects migrate from local processing and data management to distributed processing and big data storage, Project Managers are finding the need to upgrade their knowledge and skills to grasp the concepts and practices relevant to Big Data projects and opportunities. This course introduces Project Managers to the most popular Big Data processing framework: Hadoop.   In this instructor-led training, participants will learn the core components of the Hadoop ecosystem and how these technologies can be used to solve large-scale problems. In learning these foundations, participants will also improve their ability to communicate with the developers and implementers of these systems as well as the data scientists and analysts that many IT projects involve. Audience Project Managers wishing to implement Hadoop into their existing development or IT infrastructure Project Managers needing to communicate with cross-functional teams that include big data engineers, data scientists and business analysts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
samza Samza for stream processing 14小时 Apache Samza is an open-source near-realtime, asynchronous computational framework for stream processing.  It uses Apache Kafka for messaging, and Apache Hadoop YARN for fault tolerance, processor isolation, security, and resource management. This instructor-led, live training introduces the principles behind messaging systems and distributed stream processing, while walking participants through the creation of a sample Samza-based project and job execution. By the end of this training, participants will be able to: Use Samza to simplify the code needed to produce and consume messages Decouple the handling of messages from an application Use Samza to implement near-realtime asynchronous computation Use stream processing to provide a higher level of abstraction over messaging systems Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
openface OpenFace: Creating Facial Recognition Systems 14小时 OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google’s FaceNet research. In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application. By the end of this training, participants will be able to: Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation. Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc. Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
tidyverse Introduction to Data Visualization with Tidyverse and R 7小时 The Tidyverse is a collection of versatile R packages for cleaning, processing, modeling, and visualizing data. Some of the packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble. In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse. By the end of this training, participants will be able to: Perform data analysis and create appealing visualizations Draw useful conclusions from various datasets of sample data Filter, sort and summarize data to answer exploratory questions Turn processed data into informative line plots, bar plots, histograms Import and filter data from diverse data sources, including Excel, CSV, and SPSS files Audience Beginners to the R language Beginners to data analysis and data visualization Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
textsum 用Python进行文本摘要 14小时 在Python机器学习中,文本摘要功能可以读取输入文本并生成文本摘要。这个功能可以从命令行或从Python API / 库中获得。一个令人兴奋的应用是执行摘要的快速创建;这对在做报告和演讲前需要审阅大量文本数据的组织特别有用。 在这一由讲师引导的现场培训中,学员将学习使用Python创建一个简单的可自动生成输入文本摘要的应用程序。 在本次培训结束后,学员将能够: 使用一个命令行工具来总结文本。 使用Python库设计和创建文本摘要代码。 评估三个Python摘要库:sumy 0.7.0、psisummarization 1.0.4、readless 1.0.17 受众 开发人员 数据科学家 课程形式 部分讲座、部分讨论、练习和大量实操
cortana Turning Data into Intelligent Action with Cortana Intelligence 28小时 Cortana Intelligence Suite is a bundle of integrated products and services on the Microsoft Azure Cloud that enable entities to transform data into intelligent actions. In this instructor-led, live training, participants will learn how to use the components that are part of the Cortana Intelligence Suite to build data-driven intelligent applications. By the end of this training, participants will be able to: Learn how to use Cortana Intelligence Suite tools Acquire the latest knowledge of data management and analytics Use Cortana components to turn data into intelligent action Use Cortana to build applications from scratch and launch it on the cloud Audience Data scientists Programmers Developers Managers Architects Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
sspsspas Statistics with SPSS Predictive Analytics Software 14小时 Goal: Learning to work with SPSS at the level of independence The addressees: Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and learn popular data mining techniques.
hadoopmapr Hadoop Administration on MapR 28小时 Audience: This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.
hadoopdev Hadoop for Developers (4 days) 28小时 Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. This course will introduce a developer to various components (HDFS, MapReduce, Pig, Hive and HBase) Hadoop ecosystem.  
jenetics Jenetics 21小时 Jenetics is an advanced Genetic Algorithm, respectively an Evolutionary Algorithm, library written in modern day Java. Audience This course is directed at Researchers seeking to utilize Jenetics in their projects  
dsbda Data Science for Big Data Analytics 35小时 Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.
drools7int Introduction to Drools 7 for Developers 21小时 This 3 days course is aimed to introduce Drools 7 to developers.This course doesn't cover drools integration, performance or any other complex topics.
Fairseq Fairseq: Setting up a CNN-based machine translation system 7小时 Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course     Part lecture, part discussion, heavy hands-on practice Note If you wish to use specific source and target language content, please contact us to arrange.
flink Flink for scalable stream and batch data processing 28小时 Apache Flink is an open-source framework for scalable stream and batch data processing. This instructor-led, live training introduces the principles and approaches behind distributed stream and batch data processing, and walks participants through the creation of a real-time, data streaming application. By the end of this training, participants will be able to: Set up an environment for developing data analysis applications Package, execute, and monitor Flink-based, fault-tolerant, data streaming applications Manage diverse workloads Perform advanced analytics using Flink ML Set up a multi-node Flink cluster Measure and optimize performance Integrate Flink with different Big Data systems Compare Flink capabilities with those of other big data processing frameworks Audience Developers Architects Data engineers Analytics professionals Technical managers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
fiji Fiji: Introduction to scientific image processing 21小时 Fiji is an open-source image processing package that bundles ImageJ (an image processing program for scientific multidimensional images) and a number of plugins for scientific image analysis. In this instructor-led, live training, participants will learn how to use the Fiji distribution and its underlying ImageJ program to create an image analysis application. By the end of this training, participants will be able to: Use Fiji's advanced programming features and software components to extend ImageJ Stitch large 3d images from overlapping tiles Automatically update a Fiji installation on startup using the integrated update system Select from a broad selection of scripting languages to build custom image analysis solutions Use Fiji's powerful libraries, such as ImgLib on large bioimage datasets Deploy their application and collaborate with other scientists on similar projects Audience Scientists Researchers Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
nifi Apache NiFi for Administrators 21小时 Apache NiFi (Hortonworks DataFlow) is a real-time integrated data logistics and simple event processing platform that enables the moving, tracking and automation of data between systems. It is written using flow-based programming and provides a web-based user interface to manage dataflows in real time. In this instructor-led, live training, participants will learn how to deploy and manage Apache NiFi in a live lab environment. By the end of this training, participants will be able to: Install and configure Apachi NiFi Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes Automate dataflows Enable streaming analytics Apply various approaches for data ingestion Transform Big Data and into business insights Audience System administrators Data engineers Developers DevOps Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
dlfornlp Deep Learning for NLP (Natural Language Processing) 28小时 Deep Learning for NLP allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP. In this instructor-led, live training, participants will learn to use Python libraries for NLP (Natural Language Processing) as they create an application that processes a set of pictures and generates captions.  By the end of this training, participants will be able to: Design and code DL for NLP using Python libraries Create Python code that reads a substantially huge collection of pictures and generates keywords Create Python Code that generates captions from the detected keywords Audience Programmers with interest in linguistics Programmers who seek an understanding of NLP (Natural Language Processing)  Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
intelligentmobileapps Building Intelligent Mobile Applications 35小时 Intelligent applications are next generation apps that can continually learn from user interactions to provide better value and relevance to users. In this instructor-led, live training, participants will learn how to build intelligent mobile applications and bots. By the end of this training, participants will be able to: Understand the fundamental concepts of intelligent applications Learn how to use various tools for building intelligent applications Build intelligent applications using Azure, Cognitive Services API, Stream Analytics, and Xamarin Audience Developers Programmers Hobbyists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
rneuralnet Neural Network in R 14小时 This course is an introduction to applying neural networks in real world problems using R-project software.
bigdatar Programming with Big Data in R 21小时
cassdev Cassandra for Developers 21小时 This course will introduce Cassandra –  a popular NoSQL database.  It will cover Cassandra principles, architecture and data model.   Students will learn data modeling  in CQL (Cassandra Query Language) in hands-on, interactive labs.  This session also discusses Cassandra internals and some admin topics. Audience : Developers
dl4j Mastering Deeplearning4j 21小时 Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.   Audience This course is directed at engineers and developers seeking to utilize Deeplearning4j in their projects.   After this course delegates will be able to:
deeplrn 深度学习基础与实战 14小时 This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.
drools7dslba Drools 7 and DSL for Business Analysts 21小时 This 3 days course is aimed to introduce Drools 7 to Business Analysts responsible for writing tests and rules. This course focuses on creating pure logic. Analysts after this course can writing tests and logic which then can be further integrated by developers with business applications.
facebooknmt Facebook NMT: Setting up a Neural Machine Translation System 7小时 Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice Note If you wish to use specific source and target language content, please contact us to arrange.
alluxio Alluxio: Unifying disparate storage systems 7小时 Alexio is an open-source virtual distributed storage system that unifies disparate storage systems and enables applications to interact with data at memory speed. It is used by companies such as Intel, Baidu and Alibaba. In this instructor-led, live training, participants will learn how to use Alexio to bridge different computation frameworks with storage systems and efficiently manage multi-petabyte scale data as they step through the creation of an application with Alluxio. By the end of this training, participants will be able to: Develop an application with Alluxio Connect big data systems and applications while preserving one namespace Efficiently extract value from big data in any storage format Improve workload performance Deploy and manage Alluxio standalone or clustered Audience Data scientist Developer System administrator Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
kdbplusandq kdb+ and q: Analyze time series data 21小时 kdb+ is an in-memory, column-oriented database and q is its built-in, interpreted vector-based language. In kdb+, tables are columns of vectors and q is used to perform operations on the table data as if it was a list. kdb+ and q are commonly used in high frequency trading and are popular with the major financial institutions, including Goldman Sachs, Morgan Stanley, Merrill Lynch, JP Morgan, etc. In this instructor-led, live training, participants will learn how to create a time series data application using kdb+ and q. By the end of this training, participants will be able to: Understand the difference between a row-oriented database and a column-oriented database Select data, write scripts and create functions to carry out advanced analytics Analyze time series data such as stock and commodity exchange data Use kdb+'s in-memory capabilities to store, analyze, process and retrieve large data sets at high speed Think of functions and data at a higher level than the standard function(arguments) approach common in non-vector languages Explore other time-sensitive applications for kdb+, including energy trading, telecommunications, sensor data, log data, and machine and network usage monitoring Audience Developers Database engineers Data scientists Data analysts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
nifidev Apache NiFi for Developers 7小时 Apache NiFi (Hortonworks DataFlow) is a real-time integrated data logistics and simple event processing platform that enables the moving, tracking and automation of data between systems. It is written using flow-based programming and provides a web-based user interface to manage dataflows in real time. In this instructor-led, live training, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi. By the end of this training, participants will be able to: Understand NiFi's architecture and dataflow concepts Develop extensions using NiFi and third-party APIs Custom develop their own Apache Nifi processor Ingest and process real-time data from disparate and uncommon file formats and data sources Audience Developers Data engineers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
smartrobot Smart Robots for Developers 84小时 A Smart Robot is an Artificial Intelligence (AI) system that can learn from its environment and its experience and build on its capabilities based on that knowledge. Smart Robots can collaborate with humans, working along-side them and learning from their behavior. Furthermore, they have the capacity for not only manual labor, but cognitive tasks as well. In addition to physical robots, Smart Robots can also be purely software based, residing in a computer as a software application with no moving parts or physical interaction with the world. In this instructor-led, live training, participants will learn the different technologies, frameworks and techniques for programming different types of mechanical Smart Robots, then apply this knowledge to complete their own Smart Robot projects. The course is divided into 4 sections, each consisting of three days of lectures, discussions, and hands-on robot development in a live lab environment. Each section will conclude with a practical hands-on project to allow participants to practice and demonstrate their acquired knowledge. The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots. By the end of this training, participants will be able to: Understand the key concepts used in robotic technologies Understand and manage the interaction between software and hardware in a robotic system Understand and implement the software components that underpin Smart Robots Build and operate a simulated mechanical Smart Robot that can see, sense, process, grasp, navigate, and interact with humans through voice Extend a Smart Robot's ability to perform complex tasks through Deep Learning Test and troubleshoot a Smart Robot in realistic scenarios Audience Developers Engineers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note To customize any part of this course (programming language, robot model, etc.) please contact us to arrange.
aifortelecom AI Awareness for Telecom 14小时 AI is a collection of technologies for building intelligent systems capable of understanding data and the activities surrounding the data to make "intelligent decisions". For Telecom providers, building applications and services that make use of AI could open the door for improved operations and servicing in areas such as maintenance and network optimization. In this course we examine the various technologies that make up AI and the skill sets required to put them to use. Throughout the course, we examine AI's specific applications within the Telecom industry. Audience Network engineers Network operations personnel Telecom technical managers Format of the course     Part lecture, part discussion, hands-on exercises
bdbiga Big Data Business Intelligence for Govt. Agencies 35小时 Advances in technologies and the increasing amount of information are transforming how business is conducted in many industries, including government. Government data generation and digital archiving rates are on the rise due to the rapid growth of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes more complex, information management, processing, storage, security, and disposition become more complex as well. New capture, search, discovery, and analysis tools are helping organizations gain insights from their unstructured data. The government market is at a tipping point, realizing that information is a strategic asset, and government needs to protect, leverage, and analyze both structured and unstructured information to better serve and meet mission requirements. As government leaders strive to evolve data-driven organizations to successfully accomplish mission, they are laying the groundwork to correlate dependencies across events, people, processes, and information. High-value government solutions will be created from a mashup of the most disruptive technologies: Mobile devices and applications Cloud services Social business technologies and networking Big Data and analytics IDC predicts that by 2020, the IT industry will reach $5 trillion, approximately $1.7 trillion larger than today, and that 80% of the industry's growth will be driven by these 3rd Platform technologies. In the long term, these technologies will be key tools for dealing with the complexity of increased digital information. Big Data is one of the intelligent industry solutions and allows government to make better decisions by taking action based on patterns revealed by analyzing large volumes of data — related and unrelated, structured and unstructured. But accomplishing these feats takes far more than simply accumulating massive quantities of data.“Making sense of thesevolumes of Big Datarequires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information,” Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a post on the OSTP Blog. The White House took a step toward helping agencies find these technologies when it established the National Big Data Research and Development Initiative in 2012. The initiative included more than $200 million to make the most of the explosion of Big Data and the tools needed to analyze it. The challenges that Big Data poses are nearly as daunting as its promise is encouraging. Storing data efficiently is one of these challenges. As always, budgets are tight, so agencies must minimize the per-megabyte price of storage and keep the data within easy access so that users can get it when they want it and how they need it. Backing up massive quantities of data heightens the challenge. Analyzing the data effectively is another major challenge. Many agencies employ commercial tools that enable them to sift through the mountains of data, spotting trends that can help them operate more efficiently. (A recent study by MeriTalk found that federal IT executives think Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.). Custom-developed Big Data tools also are allowing agencies to address the need to analyze their data. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers find a link that can alert doctors to aortic aneurysms before they strike. It’s also used for more mundane tasks, such as sifting through résumés to connect job candidates with hiring managers.
pmml Predictive Models with PMML 7小时 The course is created to scientific, developers, analysts or any other people who want to standardize or exchange their models with Predictive Model Markup Language (PMML) file format.
hadoopba Hadoop for Business Analysts 21小时 Apache Hadoop is the most popular framework for processing Big Data. Hadoop provides rich and deep analytics capability, and it is making in-roads in to tradional BI analytics world. This course will introduce an analyst to the core components of Hadoop eco system and its analytics Audience Business Analysts Duration three days Format Lectures and hands on labs.
simplecv Computer Vision with SimpleCV 14小时 SimpleCV is an open source framework — meaning that it is a collection of libraries and software that you can use to develop vision applications. It lets you work with the images or video streams that come from webcams, Kinects, FireWire and IP cameras, or mobile phones. It’s helps you build software to make your various technologies not only see the world, but understand it too. Audience This course is directed at engineers and developers seeking to develop computer vision applications with SimpleCV.
aitech Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP 21小时 This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP.
accumulo Apache Accumulo: Building highly scalable big data applications 21小时 Apache Accumulo is a sorted, distributed key/value store that provides robust, scalable data storage and retrieval. It is based on the design of Google's BigTable and is powered by Apache Hadoop, Apache Zookeeper, and Apache Thrift.   This courses covers the working principles behind Accumulo and walks participants through the development of a sample application on Apache Accumulo. Audience Application developers Software engineers Technical consultants Format of the course Part lecture, part discussion, hands-on development and implementation, occasional tests to gauge understanding
storm Apache Storm 28小时 Apache Storm is a distributed, real-time computation engine used for enabling real-time business intelligence. It does so by enabling applications to reliably process unbounded streams of data (a.k.a. stream processing). "Storm is for real-time processing what Hadoop is for batch processing!" In this instructor-led live training, participants will learn how to install and configure Apache Storm, then develop and deploy an Apache Storm application for processing big data in real-time. Some of the topics included in this training include: Apache Storm in the context of Hadoop Working with unbounded data Continuous computation Real-time analytics Distributed RPC and ETL processing Request this course now! Audience Software and ETL developers Mainframe professionals Data scientists Big data analysts Hadoop professionals Format of the course     Part lecture, part discussion, exercises and heavy hands-on practice
apex Apache Apex: Processing big data-in-motion 21小时 Apache Apex is a YARN-native platform that unifies stream and batch processing. It processes big data-in-motion in a way that is scalable, performant, fault-tolerant, stateful, secure, distributed, and easily operable. This instructor-led, live training introduces Apache Apex's unified stream processing architecture and walks participants through the creation of a distributed application using Apex on Hadoop. By the end of this training, participants will be able to: Understand data processing pipeline concepts such as connectors for sources and sinks, common data transformations, etc. Build, scale and optimize an Apex application Process real-time data streams reliably and with minimum latency Use Apex Core and the Apex Malhar library to enable rapid application development Use the Apex API to write and re-use existing Java code Integrate Apex into other applications as a processing engine Tune, test and scale Apex applications Audience Developers Enterprise architects Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
hypertable Hypertable: Deploy a BigTable like database 14小时 Hypertable is an open-source software database management system based on the design of Google's Bigtable. In this instructor-led, live training, participants will learn how to set up and manage a Hypertable database system. By the end of this training, participants will be able to: Install, configure and upgrade a Hypertable instance Set up and administer a Hypertable cluster Monitor and optimize the performance of the database Design a Hypertable schema Work with Hypertable's API Troubleshoot operational issues Audience Developers Operations engineers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Part lecture, part discussion, exercises and heavy hands-on practice
matlabpredanalytics Matlab:用于预测性分析(Predictive Analytics) 21小时 预测性分析是使用数据分析来预测未来的过程。此过程使用数据以及数据挖掘、统计和机器学习技术创建可用来预测未来事件的预测模型。 在这一由讲师引导的现场培训中,参与者将学习如何使用Matlab建立预测模型,并将其应用于大样本数据集,以根据数据预测未来事件。 在培训结束后,参与者将能够: 创建预测模型来分析历史和交易数据中的规律 使用预测建模来识别风险和机会 建立捕捉重要趋势的数学模型 使用来自设备和业务系统的数据来减少浪费、节省时间或降低成本 受众 开发人员 工程师 领域专家 课程形式 部分讲座、部分讨论、练习和大量实操
bigdatabicriminal Big Data Business Intelligence for Criminal Intelligence Analysis 35小时 Advances in technologies and the increasing amount of information are transforming how law enforcement is conducted. The challenges that Big Data pose are nearly as daunting as Big Data's promise. Storing data efficiently is one of these challenges; effectively analyzing it is another. In this instructor-led, live training, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results. By the end of this training, participants will be able to: Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation Implement industrial big data storage and processing solutions for data analysis Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation Audience Law Enforcement specialists with a technical background Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
tableaupython Tableau with Python 14小时 Tableau is a business intelligence and data visualization tool. Python is a widely used programming language which provides support for a wide variety of statistical and machine learning techniques. Tableau's data visualization power and Python's machine learning capabilities, when combined, help developers rapidly build advanced data analytics applications for various business use cases. In this instructor-led, live training, participants will learn how to combine Tableau and Python to carry out advanced analytics. Integration of Tableau and Python will be done via the TabPy API. By the end of this training, participants will be able to: Integrate Tableau and Python using TabPy API Use the integration of Tableau and Python to analyze complex business scenarios with few lines of Python code Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
bldrools Managing Business Logic with Drools 21小时 This course is aimed at enterprise architects, business and system analysts, technical managers and developers who want to apply business rules to their solutions. This course contains a lot of simple hands-on exercises during which the participants will create working rules. Please refer to our other courses if you just need an overview of Drools. This course is usually delivered on the newest stable version of Drools and jBPM, but in case of a bespoke course, can be tailored to a specific version.

近期课程

课程日期价格【远程 / 传统课堂】
Data Mining - 北京盈科中心星期三, 2018-03-14 09:30¥29390 / ¥32990
OptaPlanner in Practice - Beijing Digital Building星期四, 2018-03-15 09:30¥18000 / ¥19200
人工智能,培训,课程,培训课程, 学习人工智能 ,人工智能讲师,人工智能私教,人工智能远程教育,学人工智能班,人工智能辅导班,人工智能晚上培训,人工智能培训师,短期人工智能培训,人工智能s辅导,一对一人工智能课程,人工智能周末培训,企业人工智能培训,人工智能训练,小组人工智能课程,人工智能老师,人工智能教程

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读者本课程适用于对统计学有一定了解并且知道如何编程R(或Python或其他所选语言)的数据科学家和统计人员。 受众本地化专家与技术背景全球内容管理本地化工程师负责实施全球内容解决方案的软件开发人员课程形式部分讲座,部分讨论,大量实践操作此培训课程面向希望将机器学习应用于实际应用的人员。 参加者将在整个课程中进行实况演练,以展示他们对所学概念的理解,并获得教师的反馈。 在培训结束后,参与者将能够:安装tensor2tensor,选择数据集,训练和评估AI模型使用Tensor2Tensor中包含的工具和组件自定义开发环境创建并使用单个模型同时学习来自多个领域的任务数量使用该模型从具有大量训练数据的任务中学习,并将这些知识应用于数据有限的任务使用单个GPU获得令人满意的处理结果读者开发人员数据专家课程形式部分讲座讨论,练习和动手练习课程可以提供任何工具,包括免费的开源数据挖掘软件和应用程序认知计算是指包含机器学习,推理,自然语言处理,语音识别和视觉(物体识别),人机交互,对话和叙述生成等的系统。 目标受众投资者和AI企业家管理人员和工程师,他们的公司正在冒险进入AI空间业务分析师和投资者本课程为管理人员,解决方案架构师,创新人员,首席技术官,软件架构师以及所有感兴趣的应用人工智能概览和最近的发展预测而创建。