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.
Naive Bayes
Multinomial models
Bayesian categorical data analysis
Discriminant analysis
Linear regression
Logistic regression
GLM
EM Algorithm
Mixed Models
Additive Models
Classification
KNN
Bayesian Graphical Models
Factor Analysis (FA)
Principal Component Analysis (PCA)
Independent Component Analysis (ICA)
Support Vector Machines (SVM) for regression and classification
Boosting
Ensemble models
Neural networks
Hidden Markov Models (HMM)
Space State Models
Clustering

OpenNN 
OpenNN: Implementing neural networks 
14小时 
OpenNN is an opensource 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 handson exercises.
Introduction to OpenNN, Machine Learning and Deep Learning
Downloading OpenNN
Working with Neural Designer
Using Neural Designer for descriptive, diagnostic, predictive and prescriptive analytics
OpenNN architecture
CPU parallelization
OpenNN classes
Data set, neural network, loss index, training strategy, model selection, testing analysis
Vector and matrix templates
Building a neural network application
Choosing a suitable neural network
Formulating the variational problem (loss index)
Solving the reduced function optimization problem (training strategy)
Working with datasets
The data matrix (columns as variables and rows as instances)
Learning tasks
Function regression
Pattern recognition
Compiling with QT Creator
Integrating, testing and debugging your application
The future of neural networks and OpenNN 
pythontextml 
Python: Machine learning with text 
14小时 
In this instructorled, live training, participants will learn how to use the right machine learning and NLP (Natural Language Processing) techniques to extract value from textbased data.
By the end of this training, participants will be able to:
Solve textbased data science problems with highquality, reusable code
Apply different aspects of scikitlearn (classification, clustering, regression, dimensionality reduction) to solve problems
Build effective machine learning models using textbased data
Create a dataset and extract features from unstructured text
Build and evaluate models to gain insight
Troubleshoot text encoding errors
Audience
Developers
Data Scientists
Format of the course
Part lecture, part discussion, exercises and heavy handson practice
To request a customized course outline for this training, please contact us. 
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.
Introduction to Applied Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
BiasVariance tradeoff
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
KNearest neighbors
Exercises
Crossvalidation and Resampling
Crossvalidation approaches
Bootstrap
Exercises
Unsupervised Learning
Kmeans clustering
Examples
Challenges of unsupervised learning and beyond Kmeans

BigData_ 
A practical introduction to Data Analysis and Big Data 
35小时 
Participants who complete this training will gain a practical, realworld understanding of Big Data and its related technologies, methodologies and tools.
Participants will have the opportunity to put this knowledge into practice through handson exercises. Group interaction and instructor feedback make up an important component of the class.
The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.
Audience
Developers / programmers
IT consultants
Format of the course
Part lecture, part discussion, handson practice and implementation, occasional quizing to measure progress.
Introduction to Data Analysis and Big Data
What makes Big Data "big"?
Velocity, Volume, Variety, Veracity (VVVV)
Limits to traditional Data Processing
Distributed Processing
Statistical Analysis
Types of Machine Learning Analysis
Data Visualization
Languages used for Data Analysis
R language
Why R for Data Analysis?
Data manipulation, calculation and graphical display
Python
Why Python for Data Analysis?
Manipulating, processing, cleaning, and crunching data
Approaches to Data Analysis
Statistical Analysis
Time Series analysis
Forecasting with Correlation and Regression models
Inferential Statistics (estimating)
Descriptive Statistics in Big Data sets (e.g. calculating mean)
Machine Learning
Supervised vs unsupervised learning
Classification and clustering
Estimating cost of specific methods
Filtering
Natural Language Processing
Processing text
Understaing meaning of the text
Automatic text generation
Sentiment/Topic Analysis
Computer Vision
Acquiring, processing, analyzing, and understanding images
Reconstructing, interpreting and understanding 3D scenes
Using image data to make decisions
Big Data infrastructure
Data Storage
Relational databases (SQL)
MySQL
Postgres
Oracle
Nonrelational databases (NoSQL)
Cassandra
MongoDB
Neo4js
Understanding the nuances
Hierarchical databases
Objectoriented databases
Documentoriented databases
Graphoriented databases
Other
Distributed Processing
Hadoop
HDFS as a distributed filesystem
MapReduce for distributed processing
Spark
Allinone inmemory cluster computing framework for largescale data processing
Structured streaming
Spark SQL
Machine Learning libraries: MLlib
Graph processing with GraphX
Scalability
Public cloud
AWS, Google, Aliyun, etc.
Private cloud
OpenStack, Cloud Foundry, etc.
Autoscalability
Choosing right solution for the problem
The future of Big Data
Closing remarks

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.
Introduction to Applied Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
BiasVariance tradeoff
Machine Learning with Python
Choice of libraries
Addon tools
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
KNearest neighbors
Exercises
Crossvalidation and Resampling
Crossvalidation approaches
Bootstrap
Exercises
Unsupervised Learning
Kmeans clustering
Examples
Challenges of unsupervised learning and beyond Kmeans

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 threeday 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.
Introduction
Simple calculations
Starting Octave, Octave as a calculator, builtin functions
The Octave environment
Named variables, numbers and formatting, number representation and accuracy, loading and saving data
Arrays and vectors
Extracting elements from a vector, vector maths
Plotting graphs
Improving the presentation, multiple graphs and figures, saving and printing figures
Octave programming I: Script files
Creating and editing a script, running and debugging scripts,
Control statements
If else, switch, for, while
Octave programming II: Functions
Matrices and vectors
Matrix, the transpose operator, matrix creation functions, building composite matrices, matrices as tables, extracting bits of matrices, basic matrix functions
Linear and Nonlinear Equations
More graphs
Putting several graphs in one window, 3D plots, changing the viewpoint, plotting surfaces, images and movies,
Eigenvectors and the Singular Value Decomposition
Complex numbers
Plotting complex numbers,
Statistics and data processing
GUI Developmen 
annmldt 
Artificial Neural Networks, Machine Learning, Deep Thinking 
21小时 
DAY 1  ARTIFICIAL NEURAL NETWORKS
Introduction and ANN Structure.
Biological neurons and artificial neurons.
Model of an ANN.
Activation functions used in ANNs.
Typical classes of network architectures .
Mathematical Foundations and Learning mechanisms.
Revisiting vector and matrix algebra.
Statespace concepts.
Concepts of optimization.
Errorcorrection learning.
Memorybased learning.
Hebbian learning.
Competitive learning.
Single layer perceptrons.
Structure and learning of perceptrons.
Pattern classifier  introduction and Bayes' classifiers.
Perceptron as a pattern classifier.
Perceptron convergence.
Limitations of a perceptrons.
Feedforward ANN.
Structures of Multilayer feedforward networks.
Back propagation algorithm.
Back propagation  training and convergence.
Functional approximation with back propagation.
Practical and design issues of back propagation learning.
Radial Basis Function Networks.
Pattern separability and interpolation.
Regularization Theory.
Regularization and RBF networks.
RBF network design and training.
Approximation properties of RBF.
Competitive Learning and Self organizing ANN.
General clustering procedures.
Learning Vector Quantization (LVQ).
Competitive learning algorithms and architectures.
Self organizing feature maps.
Properties of feature maps.
Fuzzy Neural Networks.
Neurofuzzy systems.
Background of fuzzy sets and logic.
Design of fuzzy stems.
Design of fuzzy ANNs.
Applications
A few examples of Neural Network applications, their advantages and problems will be discussed.
DAY 2 MACHINE LEARNING
The PAC Learning Framework
Guarantees for finite hypothesis set – consistent case
Guarantees for finite hypothesis set – inconsistent case
Generalities
Deterministic cv. Stochastic scenarios
Bayes error noise
Estimation and approximation errors
Model selection
Radmeacher Complexity and VC – Dimension
Bias  Variance tradeoff
Regularisation
Overfitting
Validation
Support Vector Machines
Kriging (Gaussian Process regression)
PCA and Kernel PCA
Self Organisation Maps (SOM)
Kernel induced vector space
Mercer Kernels and Kernel  induced similarity metrics
Reinforcement Learning
DAY 3  DEEP LEARNING
This will be taught in relation to the topics covered on Day 1 and Day 2
Logistic and Softmax Regression
Sparse Autoencoders
Vectorization, PCA and Whitening
SelfTaught Learning
Deep Networks
Linear Decoders
Convolution and Pooling
Sparse Coding
Independent Component Analysis
Canonical Correlation Analysis
Demos and Applications

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
Introduction to Neural Networks
Introduction to Applied Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
BiasVariance tradeoff
Machine Learning with Python
Choice of libraries
Addon tools
Machine learning Concepts and Applications
Regression
Linear regression
Generalizations and Nonlinearity
Use cases
Classification
Bayesian refresher
Naive Bayes
Logistic regression
KNearest neighbors
Use Cases
Crossvalidation and Resampling
Crossvalidation approaches
Bootstrap
Use Cases
Unsupervised Learning
Kmeans clustering
Examples
Challenges of unsupervised learning and beyond Kmeans
Short Introduction to NLP methods
word and sentence tokenization
text classification
sentiment analysis
spelling correction
information extraction
parsing
meaning extraction
question answering
Artificial Intelligence & Deep Learning
Technical Overview
R v/s Python
Caffe v/s Tensor Flow
Various Machine Learning Libraries

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.
Regression
Probabilistic Graphical Models
Boosting
Kernel Methods
Gaussian Processes
Evaluation and Model Selection
Sampling Methods
Clustering
CRFs
Random Forests
IVMs

opennmt 
OpenNMT: Setting up a Neural Machine Translation system 
7小时 
OpenNMT is a fullfeatured, opensource (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 prearranged 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 handson practice
Introduction
Why Neural Machine Translation?
Overview of the Torch project
Installation and setup
Preprocessing your data
Training the model
Translating
Using pretrained models
Working with Lua scripts
Using extensions
Troubleshooting
Joining the community
Closing remarks 
matlabml1 
MATLAB与机器学习入门 
21小时 
第一课：MATLAB入门基础
1、 简单介绍MATLAB的安装、版本历史与编程环境
2、 MATLAB基础操作（包括矩阵操作、逻辑与流程控制、函数与脚本文件、基本绘图等）
3、 文件导入（mat、txt、xls、csv等格式）
第二课：MATLAB进阶与提高
1、 MATLAB编程习惯与风格
2、 MATLAB调试技巧
3、 向量化编程与内存优化
4、 图形对象和句柄
第三课：BP神经网络
1、 BP神经网络的基本原理
2、 BP神经网络的MATLAB实现
3、 案例实践
4、 BP神经网络参数的优化
第四课：RBF、GRNN和PNN神经网络
1、 RBF神经网络的基本原理
2、 GRNN神经网络的基本原理
3、 PNN神经网络的基本原理
4、 案例实践
第五课：竞争神经网络与SOM神经网络
1、 竞争神经网络的基本原理
2、 自组织特征映射（SOM）神经网络的基本原理
3、 案例实践
第六课：支持向量机（Support Vector Machine, SVM）
1、 SVM分类的基本原理
2、 SVM回归拟合的基本原理
3、 SVM的常见训练算法（分块、SMO、增量学习等）
4、 案例实践
第七课：极限学习机（Extreme Learning Machine, ELM）
1、 ELM的基本原理
2、 ELM与BP神经网络的区别与联系
3、 案例实践
第八课：决策树与随机森林
1、 决策树的基本原理
2、 随机森林的基本原理
3、 案例实践
第九课：遗传算法（Genetic Algorithm, GA）
1、 遗传算法的基本原理
2、 常见遗传算法工具箱介绍
3、 案例实践
第十课：粒子群优化（Particle Swarm Optimization, PSO）算法
1、 粒子群优化算法的基本原理
2、 案例实践
第十一课：蚁群算法（Ant Colony Algorithm, ACA）
1、 粒子群优化算法的基本原理
2、 案例实践
第十二课：模拟退火算法（Simulated Annealing, SA）
1、 模拟退火算法的基本原理
2、 案例实践
第十三课：降维与特征选择
1、 主成分分析的基本原理
2、 偏最小二乘的基本原理
3、 常见的特征选择方法（优化搜索、Filter和Wrapper等） 
Fairsec 
Fairsec: Setting up a CNNbased machine translation system 
7小时 
Fairseq is an opensource sequencetosequence 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 handson practice
Introduction
Why Neural Machine Translation?
Overview of the Torch project
Overview of a Convolutional Neural Machine Translation model
Convolutional Sequence to Sequence Learning
Convolutional Encoder Model for Neural Machine Translation
Standard LSTMbased model
Overview of training approaches
About GPUs and CPUs
Fast beam search generation
Installation and setup
Evaluating pretrained models
Preprocessing your data
Training the model
Translating
Converting a trained model to use CPUonly operations
Joining to the community
Closing remarks 
dladv 
Advanced Deep Learning 
28小时 
Machine Learning Limitations
Machine Learning, Nonlinear mappings
Neural Networks
NonLinear Optimization, Stochastic/MiniBatch Gradient Decent
Back Propagation
Deep Sparse Coding
Sparse Autoencoders (SAE)
Convolutional Neural Networks (CNNs)
Successes: Descriptor Matching
Stereobased Obstacle
Avoidance for Robotics
Pooling and invariance
Visualization/Deconvolutional Networks
Recurrent Neural Networks (RNNs) and their optimizaiton
Applications to NLP
RNNs continued,
HessianFree Optimization
Language analysis: word/sentence vectors, parsing, sentiment analysis, etc.
Probabilistic Graphical Models
Hopfield Nets, Boltzmann machines, Restricted Boltzmann Machines
Hopfield Networks, (Restricted) Bolzmann Machines
Deep Belief Nets, Stacked RBMs
Applications to NLP , Pose and Activity Recognition in Videos
Recent Advances
LargeScale Learning
Neural Turing Machines

cpb100 
Google Cloud Platform Fundamentals: Big Data & Machine Learning 
8小时 
This oneday instructorled course introduces participants to the big data capabilities of Google Cloud Platform. Through a combination of presentations, demos, and handson 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.
The course includes presentations, demonstrations, and handson labs.
Module 1: Introducing Google Cloud Platform
Google Platform Fundamentals Overview.
Google Cloud Platform Data Products and Technology.
Usage scenarios.
Lab: Sign up for Google Cloud Platform.
Module 2: Compute and Storage Fundamentals
CPUs on demand (Compute Engine).
A global filesystem (Cloud Storage).
CloudShell.
Lab: Set up a IngestTransformPublish data processing pipeline.
Module 3: Data Analytics on the Cloud
Steppingstones to the cloud.
Cloud SQL: your SQL database on the cloud.
Lab: Importing data into CloudSQL and running queries.
Spark on Dataproc.
Lab: Machine Learning Recommendations with SparkML.
Module 4: Scaling Data Analysis
Fast random access.
Datalab.
BigQuery.
Lab: Build machine learning dataset.
Machine Learning with TensorFlow.
Lab: Train and use neural network.
Fully built models for common needs.
Lab: Employ ML APIs
Module 5: Data Processing Architectures
Messageoriented architectures with Pub/Sub.
Creating pipelines with Dataflow.
Reference architecture for realtime and batch data processing.
Module 6: Summary
Why GCP?
Where to go from here
Additional Resources

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.
Introduction to Applied Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
BiasVariance tradeoff
Machine Learning with Python
Choice of libraries
Addon tools
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
KNearest neighbors
Exercises
Crossvalidation and Resampling
Crossvalidation approaches
Bootstrap
Exercises
Unsupervised Learning
Kmeans clustering
Examples
Challenges of unsupervised learning and beyond Kmeans

cpde 
Data Engineering on Google Cloud Platform 
32小时 
This fourday instructorled class provides participants a handson introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and handon labs, participants will learn how to design data processing systems, build endtoend 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
The course includes presentations, demonstrations, and handson labs.
Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform
Module 1: Google Cloud Dataproc Overview
Creating and managing clusters.
Leveraging custom machine types and preemptible worker nodes.
Scaling and deleting Clusters.
Lab: Creating Hadoop Clusters with Google Cloud Dataproc.
Module 2: Running Dataproc Jobs
Running Pig and Hive jobs.
Separation of storage and compute.
Lab: Running Hadoop and Spark Jobs with Dataproc.
Lab: Submit and monitor jobs.
Module 3: Integrating Dataproc with Google Cloud Platform
Customize cluster with initialization actions.
BigQuery Support.
Lab: Leveraging Google Cloud Platform Services.
Module 4: Making Sense of Unstructured Data with Google’s Machine Learning APIs
Google’s Machine Learning APIs.
Common ML Use Cases.
Invoking ML APIs.
Lab: Adding Machine Learning Capabilities to Big Data Analysis.
Serverless Data Analysis with Google BigQuery and Cloud Dataflow
Module 5: Serverless data analysis with BigQuery
What is BigQuery.
Queries and Functions.
Lab: Writing queries in BigQuery.
Loading data into BigQuery.
Exporting data from BigQuery.
Lab: Loading and exporting data.
Nested and repeated fields.
Querying multiple tables.
Lab: Complex queries.
Performance and pricing.
Module 6: Serverless, autoscaling data pipelines with Dataflow
The Beam programming model.
Data pipelines in Beam Python.
Data pipelines in Beam Java.
Lab: Writing a Dataflow pipeline.
Scalable Big Data processing using Beam.
Lab: MapReduce in Dataflow.
Incorporating additional data.
Lab: Side inputs.
Handling stream data.
GCP Reference architecture.
Serverless Machine Learning with TensorFlow on Google Cloud Platform
Module 7: Getting started with Machine Learning
What is machine learning (ML).
Effective ML: concepts, types.
ML datasets: generalization.
Lab: Explore and create ML datasets.
Module 8: Building ML models with Tensorflow
Getting started with TensorFlow.
Lab: Using tf.learn.
TensorFlow graphs and loops + lab.
Lab: Using lowlevel TensorFlow + early stopping.
Monitoring ML training.
Lab: Charts and graphs of TensorFlow training.
Module 9: Scaling ML models with CloudML
Why Cloud ML?
Packaging up a TensorFlow model.
Endtoend training.
Lab: Run a ML model locally and on cloud.
Module 10: Feature Engineering
Creating good features.
Transforming inputs.
Synthetic features.
Preprocessing with Cloud ML.
Lab: Feature engineering.
Building Resilient Streaming Systems on Google Cloud Platform
Module 11: Architecture of streaming analytics pipelines
Stream data processing: Challenges.
Handling variable data volumes.
Dealing with unordered/late data.
Lab: Designing streaming pipeline.
Module 12: Ingesting Variable Volumes
What is Cloud Pub/Sub?
How it works: Topics and Subscriptions.
Lab: Simulator.
Module 13: Implementing streaming pipelines
Challenges in stream processing.
Handle late data: watermarks, triggers, accumulation.
Lab: Stream data processing pipeline for live traffic data.
Module 14: Streaming analytics and dashboards
Streaming analytics: from data to decisions.
Querying streaming data with BigQuery.
What is Google Data Studio?
Lab: build a realtime dashboard to visualize processed data.
Module 15: High throughput and lowlatency with Bigtable
What is Cloud Spanner?
Designing Bigtable schema.
Ingesting into Bigtable.
Lab: streaming into Bigtable.

predio 
Machine Learning with PredictionIO 
21小时 
PredictionIO is an open source Machine Learning Server built on top of stateoftheart open source stack.
Audience
This course is directed at developers and data scientists who want to create predictive engines for any machine learning task.
Getting Started
Quick Intro
Installation Guide
Downloading Template
Deploying an Engine
Customizing an Engine
App Integration Overview
Developing PredictionIO
System Architecture
Event Server Overview
Collecting Data
Learning DASE
Implementing DASE
Evaluation Overview
Intellij IDEA Guide
Scala API
Machine Learning Education and Usage Examples
Comics Recommendation
Text Classification
Community Contributed Demo
Dimensionality Reducation and usage
PredictionIO SDKs (Select One)
Java
PHP
Python
Ruby
Community Contributed

Fairseq 
Fairseq: Setting up a CNNbased machine translation system 
7小时 
Fairseq is an opensource sequencetosequence 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 handson practice
Introduction
Why Neural Machine Translation?
Overview of the Torch project
Overview of a Convolutional Neural Machine Translation model
Convolutional Sequence to Sequence Learning
Convolutional Encoder Model for Neural Machine Translation
Standard LSTMbased model
Overview of training approaches
About GPUs and CPUs
Fast beam search generation
Installation and setup
Evaluating pretrained models
Preprocessing your data
Training the model
Translating
Converting a trained model to use CPUonly operations
Joining to the community
Closing remarks 
systemml 
Apache SystemML for Machine Learning 
14小时 
Apache SystemML is a distributed and declarative machine learning platform.
SystemML provides declarative largescale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, inmemory 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.
Running SystemML
Standalone
Spark MLContext
Spark Batch
Hadoop Batch
JMLC
Tools
Debugger
IDE
Troubleshooting
Languages and ML Algorithms
DML
PyDML
Algorithms

facebooknmt 
Facebook NMT: Setting up a neural machine translation system 
7小时 
Fairseq is an opensource sequencetosequence 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 handson practice
Note
If you wish to use specific source and target language content, please contact us to arrange.
Introduction
Why Neural Machine Translation?
Borrowing from image recognition techniques
Overview of the Torch and Caffe2 projects
Overview of a Convolutional Neural Machine Translation model
Convolutional Sequence to Sequence Learning
Convolutional Encoder Model for Neural Machine Translation
Standard LSTMbased model
Overview of training approaches
About GPUs and CPUs
Fast beam search generation
Installation and setup
Evaluating pretrained models
Preprocessing your data
Training the model
Translating
Converting a trained model to use CPUonly operations
Joining to the community
Closing remarks 
aiintrozero 
From Zero to AI 
35小时 
This course is created for people who have no previous experience in probability and statistics.
Probability (3.5h)
Definition of probability
Binomial distribution
Everyday usage exercises
Statistics (10.5h)
Descriptive Statistics
Inferential Statistics
Regression
Logistic Regression
Exercises
Intro to programming (3.5h)
Procedural Programming
Functional Programming
OOP Programming
Exercises (writing logic for a game of choice, e.g. noughts and crosses)
Machine Learning (10.5h)
Classification
Clustering
Neural Networks
Exercises (write AI for a computer game of choice)
Rules Engines and Expert Systems (7 hours)
Intro to Rule Engines
Write AI for the same game and combine solutions into hybrid approach

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 instructorled, 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 highquality end models by first modeling noisy training sets
Use Snorkel to implement weak supervision techniques and apply data programming to weaklysupervised machine learning systems
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy handson practice
To request a customized course outline for this training, please contact us.

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.
Current state of the technology
What is used
What may be potentially used
Rules based AI
Simplifying decision
Machine Learning
Classification
Clustering
Neural Networks
Types of Neural Networks
Presentation of working examples and discussion
Deep Learning
Basic vocabulary
When to use Deep Learning, when not to
Estimating computational resources and cost
Very short theoretical background to Deep Neural Networks
Deep Learning in practice (mainly using TensorFlow)
Preparing Data
Choosing loss function
Choosing appropriate type on neural network
Accuracy vs speed and resources
Training neural network
Measuring efficiency and error
Sample usage
Anomaly detection
Image recognition
ADAS

dsstne 
Amazon DSSTNE: Build a recommendation system 
7小时 
Amazon DSSTNE is an opensource 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 instructorled, 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 modelparallel fashion
Generate Amazonlike personalized product recommendations
Deploy a productionready application that can scale at heavy workloads
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy handson practice
To request a customized course outline for this training, please contact us.

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.
TensorFlow Basics
Creation, Initializing, Saving, and Restoring TensorFlow variables
Feeding, Reading and Preloading TensorFlow Data
How to use TensorFlow infrastructure to train models at scale
Visualizing and Evaluating models with TensorBoard
TensorFlow Mechanics
Inputs and Placeholders
Build the GraphS
Inference
Loss
Training
Train the Model
The Graph
The Session
Train Loop
Evaluate the Model
Build the Eval Graph
Eval Output
The Perceptron
Activation functions
The perceptron learning algorithm
Binary classification with the perceptron
Document classification with the perceptron
Limitations of the perceptron
From the Perceptron to Support Vector Machines
Kernels and the kernel trick
Maximum margin classification and support vectors
Artificial Neural Networks
Nonlinear decision boundaries
Feedforward and feedback artificial neural networks
Multilayer perceptrons
Minimizing the cost function
Forward propagation
Back propagation
Improving the way neural networks learn
Convolutional Neural Networks
Goals
Model Architecture
Principles
Code Organization
Launching and Training the Model
Evaluating a Model

pythonadvml 
Python for Advanced Machine Learning 
21小时 
In this instructorled, live training, participants will learn the most relevant and cuttingedge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.
By the end of this training, participants will be able to:
Implement machine learning algorithms and techniques for solving complex problems
Apply deep learning and semisupervised learning to applications involving image, music, text, and financial data
Push Python algorithms to their maximum potential
Use libraries and packages such as NumPy and Theano
Audience
Developers
Analysts
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy handson practice
To request a customized course outline for this training, please contact us. 
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 handson exercises, instructor feedback, and testing of knowledge and skills acquired.
Audience
Data analysts
PhD students, researchers and practitioners
Introduction
Probability theory, model selection, decision and information theory
Probability distributions
Linear models for regression and classification
Neural networks
Kernel methods
Sparse kernel machines
Graphical models
Mixture models and EM
Approximate inference
Sampling methods
Continuous latent variables
Sequential data
Combining models

radvml 
Advanced Machine Learning with R 
21小时 
In this instructorled, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a realworld application.
By the end of this training, participants will be able to:
Use techniques as hyperparameter 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 handson practice
To request a customized course outline for this training, please contact us. 
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.
Introduction
Computer Vision
Machine Vision
Pattern Matching vs Pattern Recognition
Alignment
Features of the target object
Points of reference on the object
Determining position
Determining orientation
Gauging
Setting tolerance levels
Measuring lengths, diameters, angles, and other dimensions
Rejecting a component
Inspection
Detecting flaws
Adjusting the system
Closing remarks

encogintro 
Encog: Introduction to Machine Learning 
14小时 
Encog is an opensource machine learning framework for Java and .Net.
In this instructorled, live training, participants will learn how to create various neural network components using ENCOG. Realworld 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 realworld applications
Audience
Developers
Analysts
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy handson practice
To request a customized course outline for this training, please contact us. 
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.
Naive Bayes
Multinomial models
Bayesian categorical data analysis
Discriminant analysis
Linear regression
Logistic regression
GLM
EM Algorithm
Mixed Models
Additive Models
Classification
KNN
Ridge regression
Clustering

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 realworld applications. We step through numerous handson 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
Inclass coding and integration exercises
Test questions sprinkled along the way to check understanding
Introduction to Torch
Like NumPy but with CPU and GPU implementation
Torch's usage in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking
Installing Torch
Linux, Windows, Mac
Bitmapi and Docker
Installing Torch packages
Using the LuaRocks package manager
Choosing an IDE for Torch
ZeroBrane Studio
Eclipse plugin for Lua
Working with the Lua scripting language and LuaJIT
Lua's integration with C/C++
Lua syntax: datatypes, loops and conditionals, functions, functions, tables, and file i/o.
Object orientation and serialization in Torch
Coding exercise
Loading a dataset in Torch
MNIST
CIFAR10, CIFAR100
Imagenet
Machine Learning in Torch
Deep Learning
Manual feature extraction vs convolutional networks
Supervised and Unsupervised Learning
Building a neural network with Torch
Ndimensional arrays
Image analysis with Torch
Image package
The Tensor library
Working with the REPL interpreter
Working with databases
Networking and Torch
GPU support in Torch
Integrating Torch
C, Python, and others
Embedding Torch
iOS and Android
Other frameworks and libraries
Facebook's optimized deeplearning modules and containers
Creating your own package
Testing and debugging
Releasing your application
The future of AI and Torch 
encogadv 
Encog: Advanced Machine Learning 
14小时 
Encog is an opensource machine learning framework for Java and .Net.
In this instructorled, 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 handson practice
To request a customized course outline for this training, please contact us. 