aiintrozero 
From Zero to AI 
35 hours 
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

facebooknmt 
Facebook NMT: Setting up a Neural Machine Translation System 
7 hours 
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 
aiauto 
Artificial Intelligence in Automotive 
14 hours 
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

snorkel 
Snorkel: Rapidly process training data 
7 hours 
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.

Neuralnettf 
Neural Networks Fundamentals using TensorFlow as Example 
28 hours 
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

dsstne 
Amazon DSSTNE: Build a recommendation system 
7 hours 
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.

MLFWR1 
Machine Learning Fundamentals with R 
14 hours 
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

datamodeling 
Pattern Recognition 
35 hours 
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

pythonadvml 
Python for Advanced Machine Learning 
21 hours 
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. 
mlfunpython 
Machine Learning Fundamentals with Python 
14 hours 
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

patternmatching 
Pattern Matching 
14 hours 
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

radvml 
Advanced Machine Learning with R 
21 hours 
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. 
appliedml 
Applied Machine Learning 
14 hours 
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

Torch 
Torch: Getting started with Machine and Deep Learning 
21 hours 
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 
encogintro 
Encog: Introduction to Machine Learning 
14 hours 
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 hours 
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

OpenNN 
OpenNN: Implementing neural networks 
14 hours 
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 
encogadv 
Encog: Advanced Machine Learning 
14 hours 
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. 
annmldt 
Artificial Neural Networks, Machine Learning, Deep Thinking 
21 hours 
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

mldt 
Machine Learning and Deep Learning 
21 hours 
This course covers AI (emphasizing Machine Learning and Deep Learning)
Machine learning
Introduction to Machine Learning
Applications of machine learning
Supervised Versus Unsupervised Learning
Machine Learning Algorithms
Regression
Classification
Clustering
Recommender System
Anomaly Detection
Reinforcement Learning
Regression
Simple & Multiple Regression
Least Square Method
Estimating the Coefficients
Assessing the Accuracy of the Coefficient Estimates
Assessing the Accuracy of the Model
Post Estimation Analysis
Other Considerations in the Regression Models
Qualitative Predictors
Extensions of the Linear Models
Potential Problems
Biasvariance trade off [underfitting/overfitting] for regression models
Resampling Methods
CrossValidation
The Validation Set Approach
LeaveOneOut CrossValidation
kFold CrossValidation
BiasVariance TradeOff for kFold
The Bootstrap
Model Selection and Regularization
Subset Selection [Best Subset Selection, Stepwise Selection, Choosing the Optimal Model]
Shrinkage Methods/ Regularization [Ridge Regression, Lasso & Elastic Net]
Selecting the Tuning Parameter
Dimension Reduction Methods
Principal Components Regression
Partial Least Squares
Classification
Logistic Regression
The Logistic Model cost function
Estimating the Coefficients
Making Predictions
Odds Ratio
Performance Evaluation Matrices
[Sensitivity/Specificity/PPV/NPV, Precision, ROC curve etc.]
Multiple Logistic Regression
Logistic Regression for >2 Response Classes
Regularized Logistic Regression
Linear Discriminant Analysis
Using Bayes’ Theorem for Classification
Linear Discriminant Analysis for p=1
Linear Discriminant Analysis for p >1
Quadratic Discriminant Analysis
KNearest Neighbors
Classification with Nonlinear Decision Boundaries
Support Vector Machines
Optimization Objective
The Maximal Margin Classifier
Kernels
OneVersusOne Classification
OneVersusAll Classification
Comparison of Classification Methods
Introduction to Deep Learning
ANN Structure
Biological neurons and artificial neurons
Nonlinear Hypothesis
Model Representation
Examples & Intuitions
Transfer Function/ Activation Functions
Typical classes of network architectures
Feed forward ANN.
Structures of Multilayer feed forward networks
Back propagation algorithm
Back propagation  training and convergence
Functional approximation with back propagation
Practical and design issues of back propagation learning
Deep Learning
Artificial Intelligence & Deep Learning
Softmax Regression
SelfTaught Learning
Deep Networks
Demos and Applications
Lab:
Getting Started with R
Introduction to R
Basic Commands & Libraries
Data Manipulation
Importing & Exporting data
Graphical and Numerical Summaries
Writing functions
Regression
Simple & Multiple Linear Regression
Interaction Terms
Nonlinear Transformations
Dummy variable regression
CrossValidation and the Bootstrap
Subset selection methods
Penalization [Ridge, Lasso, Elastic Net]
Classification
Logistic Regression, LDA, QDA, and KNN,
Resampling & Regularization
Support Vector Machine
Resampling & Regularization
Note:
For ML algorithms, case studies will be used to discuss their application, advantages & potential issues.
Analysis of different data sets will be performed using R

pythontextml 
Python: Machine Learning with Text 
21 hours 
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
Visualize data with Matplotlib
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
Introduction
The value of textbased data
Workflow for a TextBased Data Science Problem
Choosing the Right Machine Learning Libraries
Overview of NLP Techniques
Preparing a Dataset
Visualizing the Data
Working with Text Data with scikitlearn
Building a Machine Learning Model
Splitting into Train and Test Sets
Applying Linear Regression and NonLinear Regression
Applying NLP Techniques
Parsing Text Data Using Regular Expressions
Exploring Other Machine Language Approaches
Troubleshooting Text Encoding Issues
Closing Remarks 
wdneo4j 
Wprowadzenie do Neo4j  grafowej bazy danych 
7 hours 

dmmlr 
Data Mining & Machine Learning with R 
14 hours 
R is an opensource 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.
Introduction to Data mining and Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
BiasVariance tradeoff
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Dicriminant analysis
Logistic regression
KNearest neighbors
Support Vector Machines
Neural networks
Decision trees
Exercises
Crossvalidation and Resampling
Crossvalidation approaches
Bootstrap
Exercises
Unsupervised Learning
Kmeans clustering
Examples
Challenges of unsupervised learning and beyond Kmeans
Advanced topics
Ensemble models
Mixed models
Boosting
Examples
Multidimensional reduction
Factor Analysis
Principal Component Analysis
Examples

mlios 
Machine Learning on iOS 
14 hours 
In this instructorled, 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 pretrained 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, scikit learn, libsvm, Anaconda, and Spyder
Audience
Developers
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. 
mlrobot1 
Machine Learning for Robotics 
21 hours 
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

BigData_ 
A practical introduction to Data Analysis and Big Data 
35 hours 
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 analysis / 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 the right solution for the problem
The future of Big Data
Closing remarks

matlabdl 
Matlab for Deep Learning 
14 hours 
In this instructorled, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.
By the end of this training, participants will be able to:
Build a deep learning model
Automate data labeling
Work with models from Caffe and TensorFlowKeras
Train data using multiple GPUs, the cloud, or clusters
Audience
Developers
Engineers
Domain experts
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. 
matlabml1 
Introduction to Machine Learning with MATLAB 
21 hours 
MATLAB is a numerical computing environment and programming language developed by MathWorks.
MATLAB Basics
MATLAB More Advanced Features
BP Neural Network
RBF, GRNN and PNN Neural Networks
SOM Neural Networks
Support Vector Machine, SVM
Extreme Learning Machine, ELM
Decision Trees and Random Forests
Genetic Algorithm, GA
Particle Swarm Optimization, PSO
Ant Colony Algorithm, ACA
Simulated Annealing, SA
Dimenationality Reduction and Feature Selection

octnp 
Octave not only for programmers 
21 hours 
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 Development 
mlbankingr 
Machine Learning for Banking (with R) 
28 hours 
In this instructorled, live training, participants will learn how to apply machine learning techniques and tools for solving realworld problems in the banking 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 live projects.
Audience
Developers
Data scientists
Banking professionals with a technical background
Format of the course
Part lecture, part discussion, exercises and heavy handson practice
Introduction
Difference between statistical learning (statistical analysis) and machine learning
Adoption of machine learning technology by finance and banking companies
Different Types of Machine Learning
Supervised learning vs unsupervised learning
Iteration and evaluation
Biasvariance tradeoff
Combining supervised and unsupervised learning (semisupervised learning)
Machine Learning Languages and Toolsets
Open source vs proprietary systems and software
R vs Python vs Matlab
Libraries and frameworks
Machine Learning Case Studies
Consumer data and big data
Assessing risk in consumer and business lending
Improving customer service through sentiment analysis
Detecting identity fraud, billing fraud and money laundering
Introduction to R
Installing the RStudio IDE
Loading R packages
Data structures
Vectors
Factors
Lists
Data Frames
Matrixes and Arrays
How to Load Machine Learning Data
Databases, data warehouses and streaming data
Distributed storage and processing with Hadoop and Spark
Importing data from a database
Importing data from Excel and CSV
Modeling Business Decisions with Supervised Learning
Classifying your data (classification)
Using regression analysis to predict outcome
Choosing from available machine learning algorithms
Understanding decision tree algorithms
Understanding random forest algorithms
Model evaluation
Exercise
Regression Analysis
Linear regression
Generalizations and Nonlinearity
Exercise
Classification
Bayesian refresher
Naive Bayes
Logistic regression
KNearest neighbors
Exercise
Handson: Building an Estimation Model
Assessing lending risk based on customer type and history
Evaluating the performance of Machine Learning Algorithms
Crossvalidation and resampling
Bootstrap aggregation (bagging)
Exercise
Modeling Business Decisions with Unsupervised Learning
When sample data sets are not available
Kmeans clustering
Challenges of unsupervised learning
Beyond Kmeans
Bayes networks and Markov Hidden Models
Exercise
Handson: Building a Recommendation System
Analyzing past customer behavior to improve new service offerings
Extending your company's capabilities
Developing models in the cloud
Accelerating machine learning with additional GPUs
Applying Deep Learning neural networks for computer vision, voice recognition and text analysis
Closing Remarks 
dladv 
Advanced Deep Learning 
28 hours 
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

mlentre 
Machine Learning Concepts for Entrepreneurs and Managers 
21 hours 
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

mlbankingpython_ 
Machine Learning for Banking (with Python) 
21 hours 
In this instructorled, live training, participants will learn how to apply machine learning techniques and tools for solving realworld problems in the banking industry. Python 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.
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy handson practice
Introduction
Difference between statistical learning (statistical analysis) and machine learning
Adoption of machine learning technology and talent by finance and banking companies
Different Types of Machine Learning
Supervised learning vs unsupervised learning
Iteration and evaluation
Biasvariance tradeoff
Combining supervised and unsupervised learning (semisupervised learning)
Machine Learning Languages and Toolsets
Open source vs proprietary systems and software
Python vs R vs Matlab
Libraries and frameworks
Machine Learning Case Studies
Consumer data and big data
Assessing risk in consumer and business lending
Improving customer service through sentiment analysis
Detecting identity fraud, billing fraud and money laundering
Handson: Python for Machine Learning
Preparing the Development Environment
Obtaining Python machine learning libraries and packages
Working with scikitlearn and PyBrain
How to Load Machine Learning Data
Databases, data warehouses and streaming data
Distributed storage and processing with Hadoop and Spark
Exported data and Excel
Modeling Business Decisions with Supervised Learning
Classifying your data (classification)
Using regression analysis to predict outcome
Choosing from available machine learning algorithms
Understanding decision tree algorithms
Understanding random forest algorithms
Model evaluation
Exercise
Regression Analysis
Linear regression
Generalizations and Nonlinearity
Exercise
Classification
Bayesian refresher
Naive Bayes
Logistic regression
KNearest neighbors
Exercise
Handson: Building an Estimation Model
Assessing lending risk based on customer type and history
Evaluating the performance of Machine Learning Algorithms
Crossvalidation and resampling
Bootstrap aggregation (bagging)
Exercise
Modeling Business Decisions with Unsupervised Learning
When sample data sets are not available
Kmeans clustering
Challenges of unsupervised learning
Beyond Kmeans
Bayes networks and Markov Hidden Models
Exercise
Handson: Building a Recommendation System
Analyzing past customer behavior to improve new service offerings
Extending your company's capabilities
Developing models in the cloud
Accelerating machine learning with GPU
Applying Deep Learning neural networks for computer vision, voice recognition and text analysis
Closing Remarks 
mlfsas 
Machine Learning Fundamentals with Scala and Apache Spark 
14 hours 
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

opennmt 
OpenNMT: Setting up a Neural Machine Translation System 
7 hours 
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 
opennlp 
OpenNLP for Text Based Machine Learning 
14 hours 
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, partofspeech tagging, named entity extraction, chunking, parsing and coreference resolution.
In this instructorled, 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 handson practice
Introduction to Machine Learning and Natural Language Processing
Installing and Configuring OpenNLP
Overview of OpenNLP's Library Structure
Downloading Existing Models
Calling the OpenNLP's APIs
Sentence Detection and Tokenization
PartofSpeach (POS) Tagging
Phrase Chunking
Parsing
Name Finding
English Coreference
Training the Tools
Creating a Model from Scratch
Extending OpenNLP
Closing remarks 
cpb100 
Google Cloud Platform Fundamentals: Big Data & Machine Learning 
8 hours 
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

Fairsec 
Fairsec: Setting up a CNNbased machine translation system 
7 hours 
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 
undnn 
Understanding Deep Neural Networks 
35 hours 
This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).
Part1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.
Part2(20%) of this training introduces Theano  a python library that makes writing deep learning models easy.
Part3(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.
Part 1 – Deep Learning and DNN Concepts
Introduction AI, Machine Learning & Deep Learning
History, basic concepts and usual applications of artificial intelligence far Of the fantasies carried by this domain
Collective Intelligence: aggregating knowledge shared by many virtual agents
Genetic algorithms: to evolve a population of virtual agents by selection
Usual Learning Machine: definition.
Types of tasks: supervised learning, unsupervised learning, reinforcement learning
Types of actions: classification, regression, clustering, density estimation, reduction of dimensionality
Examples of Machine Learning algorithms: Linear regression, Naive Bayes, Random Tree
Machine learning VS Deep Learning: problems on which Machine Learning remains Today the state of the art (Random Forests & XGBoosts)
Basic Concepts of a Neural Network (Application: multilayer perceptron)
Reminder of mathematical bases.
Definition of a network of neurons: classical architecture, activation and
Weighting of previous activations, depth of a network
Definition of the learning of a network of neurons: functions of cost, backpropagation, Stochastic gradient descent, maximum likelihood.
Modeling of a neural network: modeling input and output data according to The type of problem (regression, classification ...). Curse of dimensionality.
Distinction between Multifeature data and signal. Choice of a cost function according to the data.
Approximation of a function by a network of neurons: presentation and examples
Approximation of a distribution by a network of neurons: presentation and examples
Data Augmentation: how to balance a dataset
Generalization of the results of a network of neurons.
Initialization and regularization of a neural network: L1 / L2 regularization, Batch Normalization
Optimization and convergence algorithms
Standard ML / DL Tools
A simple presentation with advantages, disadvantages, position in the ecosystem and use is planned.
Data management tools: Apache Spark, Apache Hadoop Tools
Machine Learning: Numpy, Scipy, Scikit
DL high level frameworks: PyTorch, Keras, Lasagne
Low level DL frameworks: Theano, Torch, Caffe, Tensorflow
Convolutional Neural Networks (CNN).
Presentation of the CNNs: fundamental principles and applications
Basic operation of a CNN: convolutional layer, use of a kernel,
Padding & stride, feature map generation, pooling layers. Extensions 1D, 2D and 3D.
Presentation of the different CNN architectures that brought the state of the art in classification
Images: LeNet, VGG Networks, Network in Network, Inception, Resnet. Presentation of Innovations brought about by each architecture and their more global applications (Convolution 1x1 or residual connections)
Use of an attention model.
Application to a common classification case (text or image)
CNNs for generation: superresolution, pixeltopixel segmentation. Presentation of
Main strategies for increasing feature maps for image generation.
Recurrent Neural Networks (RNN).
Presentation of RNNs: fundamental principles and applications.
Basic operation of the RNN: hidden activation, back propagation through time, Unfolded version.
Evolutions towards the Gated Recurrent Units (GRUs) and LSTM (Long Short Term Memory).
Presentation of the different states and the evolutions brought by these architectures
Convergence and vanising gradient problems
Classical architectures: Prediction of a temporal series, classification ...
RNN Encoder Decoder type architecture. Use of an attention model.
NLP applications: word / character encoding, translation.
Video Applications: prediction of the next generated image of a video sequence.
Generational models: Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN).
Presentation of the generational models, link with the CNNs
Autoencoder: reduction of dimensionality and limited generation
Variational Autoencoder: generational model and approximation of the distribution of a given. Definition and use of latent space. Reparameterization trick. Applications and Limits observed
Generative Adversarial Networks: Fundamentals.
Dual Network Architecture (Generator and discriminator) with alternate learning, cost functions available.
Convergence of a GAN and difficulties encountered.
Improved convergence: Wasserstein GAN, Began. Earth Moving Distance.
Applications for the generation of images or photographs, text generation, superresolution.
Deep Reinforcement Learning.
Presentation of reinforcement learning: control of an agent in a defined environment
By a state and possible actions
Use of a neural network to approximate the state function
Deep Q Learning: experience replay, and application to the control of a video game.
Optimization of learning policy. Onpolicy && offpolicy. Actor critic architecture. A3C.
Applications: control of a single video game or a digital system.
Part 2 – Theano for Deep Learning
Theano Basics
Introduction
Installation and Configuration
Theano Functions
inputs, outputs, updates, givens
Training and Optimization of a neural network using Theano
Neural Network Modeling
Logistic Regression
Hidden Layers
Training a network
Computing and Classification
Optimization
Log Loss
Testing the model
Part 3 – DNN using 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
Prepare the Data
Download
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
Basic Introductions to be given to the below modules(Brief Introduction to be provided based on time availability):
Tensorflow  Advanced Usage
Threading and Queues
Distributed TensorFlow
Writing Documentation and Sharing your Model
Customizing Data Readers
Manipulating TensorFlow Model Files
TensorFlow Serving
Introduction
Basic Serving Tutorial
Advanced Serving Tutorial
Serving Inception Model Tutorial

predio 
Machine Learning with PredictionIO 
21 hours 
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

cpde 
Data Engineering on Google Cloud Platform 
32 hours 
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.

textsum 
Text Summarization with Python 
14 hours 
In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the commandline or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations.
In this instructorled, live training, participants will learn to use Python to create a simple application that autogenerates a summary of input text.
By the end of this training, participants will be able to:
Use a commandline tool that summarizes text.
Design and create Text Summarization code using Python libraries.
Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17
Audience
Developers
Data Scientists
Format of the course
Part lecture, part discussion, exercises and heavy handson practice
Introduction to Text Summarization with Python
Comparing sample text with autogenerated summaries
Installing sumy (a Python CommandLine Executable for Text Summarization)
Using sumy as a CommandLine Text Summarization Utility (HandsOn Exercise)
Evaluating three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17 based on documented features
Choosing a library: sumy, pysummarization or readless
Creating a Python application using sumy library on Python 2.7/3.3+
Installing the sumy library for Text Summarization
Using the Edmundson (Extraction) method in sumy Python Library for Text
Summarization
Creating simple Python test code that uses sumy library to generate a text summary
Creating a Python application using pysummarization library on Python 2.7/3.3+
Installing pysummarization library for Text Summarization
Using the pysummarization library for Text Summarization
Creating simple Python test code that uses pysummarization library to generate a text summary
Creating a Python application using readless library on Python 2.7/3.3+
Installing readless library for Text Summarization
Using the readless library for Text Summarization
Creating simple Python test code that uses readless library to generate a text summary
Troubleshooting and debugging
Closing Remarks 
systemml 
Apache SystemML for Machine Learning 
14 hours 
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

Fairseq 
Fairseq: Setting up a CNNbased machine translation system 
7 hours 
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 
mlfinancepython 
Machine Learning for Finance (with Python) 
21 hours 
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed.
In this instructorled, live training, participants will learn how to apply machine learning techniques and tools for solving realworld problems in the finance industry. Python 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 Python
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy handson practice
Introduction
Difference between statistical learning (statistical analysis) and machine learning
Adoption of machine learning technology and talent by finance companies
Understanding Different Types of Machine Learning
Supervised learning vs unsupervised learning
Iteration and evaluation
Biasvariance tradeoff
Combining supervised and unsupervised learning (semisupervised learning)
Understanding Machine Learning Languages and Toolsets
Open source vs proprietary systems and software
Python vs R vs Matlab
Libraries and frameworks
Understanding Neural Networks
Understanding Basic Concepts in Finance
Understanding Stocks Trading
Understanding Time Series Data
Understanding Financial Analyses
Machine Learning Case Studies in Finance
Signal Generation and Testing
Feature Engineering
Artificial Intelligence Algorithmic Trading
Quantitative Trade Predictions
RoboAdvisors for Portfolio Management
Risk Management and Fraud Detection
Insurance Underwriting
Handson: Python for Machine Learning
Setting Up the Workspace
Obtaining Python machine learning libraries and packages
Working with Pandas
Working with ScikitLearn
Importing Financial Data into Python
Using Pandas
Using Quandl
Integrating with Excel
Working with Time Series Data with Python
Exploring Your Data
Visualizing Your Data
Implementing Common Financial Analyses with Python
Returns
Moving Windows
Volatility Calculation
Ordinary LeastSquares Regression (OLS)
Developing an Algorithmic Trading Strategy Using Supervised Machine Learning with Python
Understanding the Momentum Trading Strategy
Understanding the Reversion Trading Strategy
Implementing Your Simple Moving Averages (SMA) Trading Strategy
Backtesting Your Machine Learning Trading Strategy
Learning Backtesting Pitfalls
Components of Your Backtester
Using Python Backtesting Tools
Implementing Your Simple Backtester
Improving Your Machine Learning Trading Strategy
KMeans
kNearest Neighbors (KNN)
Classification or Regression Trees
Genetic Algorithm
Working with MultiSymbol Portfolios
Using a Risk Management Framework
Using EventDriven Backtesting
Evaluating Your Machine Learning Trading Strategy's Performance
Using the Sharpe Ratio
Calculating a Maximum Drawdown
Using Compound Annual Growth Rate (CAGR)
Measuring Distribution of Returns
Using TradeLevel Metrics
Summary
Troubleshooting
Closing Remarks 