预测分析培训
Predictive Analytics培训，预测分析培训
客户评论
他的内容非常丰富，乐于助人。
Pratheep Ravy
课程名称: Predictive Modelling with R
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材料范围Maci
Maciej Jonczyk
课程名称: From Data to Decision with Big Data and Predictive Analytics
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系统化ML领域的知识 - Orange P
Orange Polska
课程名称: From Data to Decision with Big Data and Predictive Analytics
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以后使用的材料非常好。
PAUL BEALES
课程名称: Applied Machine Learning
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Michael的培训师对大数据和R这个主题非常熟悉和熟练。他非常灵活，可以快速定制培训以满足客户的需求。他也非常有能力在旅途中解决技术和主题问题。神奇和专业的训练！
Xiaoyuan Geng
课程名称: Programming with Big Data in R
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引入新的软件包。
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
课程名称: Programming with Big Data in R
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导师Michael Yan先生很好地与观众互动，指导很明确。导师也可以根据培训期间学生的要求添加更多信息。
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
课程名称: Programming with Big Data in R
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主题和速度是完美的
Tim
课程名称: Programming with Big Data in R
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课程简介
| 代码 | 名字 | 时长 | 概览 |
|---|---|---|---|
| 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. |
| 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. |
| 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. |
| bigdatar | Programming with Big Data in R | 21小时 | Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry. |
| 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 |
| 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 |
| 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. |
| 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。 |
| 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 |
| 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 |
| bigdataanahealth | Big Data Analytics in Health | 21小时 | Big data analytics involves the process of examining large amounts of varied data sets in order to uncover correlations, hidden patterns, and other useful insights. The health industry has massive amounts of complex heterogeneous medical and clinical data. Applying big data analytics on health data presents huge potential in deriving insights for improving delivery of healthcare. However, the enormity of these datasets poses great challenges in analyses and practical applications to a clinical environment. In this instructor-led, live training (remote), participants will learn how to perform big data analytics in health as they step through a series of hands-on live-lab exercises. By the end of this training, participants will be able to: Install and configure big data analytics tools such as Hadoop MapReduce and Spark Understand the characteristics of medical data Apply big data techniques to deal with medical data Study big data systems and algorithms in the context of health applications Audience Developers Data Scientists Format of the Course Part lecture, part discussion, exercises and heavy hands-on practice. Note To request a customized training for this course, please contact us to arrange. |