flexibility
Werner Philipp  Robert Bosch GmbH
Machine Learning courses
Code  Name  Venue  Duration  Course Date  PHP  Course Price [Remote / Classroom] 

Neuralnettf  Neural Networks Fundamentals using TensorFlow as Example  Southampton  28 hours  Tue, 20180130 09:30  £5200 / £6200  
MLFWR1  Machine Learning Fundamentals with R  Southampton  14 hours  Tue, 20180130 09:30  £2600 / £3100  
dsstne  Amazon DSSTNE: Build a recommendation system  Southampton  7 hours  Fri, 20180202 09:30  £1100 / £1350  
datamodeling  Pattern Recognition  Southampton  35 hours  Mon, 20180205 09:30  £6500 / £7750  
encogadv  Encog: Advanced Machine Learning  Southampton  14 hours  Tue, 20180206 09:30  £2200 / £2700  
opennlp  OpenNLP for Text Based Machine Learning  Southampton  14 hours  Wed, 20180207 09:30  £2200 / £2700  
aiauto  Artificial Intelligence in Automotive  Southampton  14 hours  Thu, 20180208 09:30  £2600 / £3100  
mlrobot1  Machine Learning for Robotics  Southampton  21 hours  Mon, 20180212 09:30  £3300 / £4050  
mlbankingpython_  Machine Learning for Banking (with Python)  Southampton  21 hours  Mon, 20180212 09:30  £3300 / £4050  
undnn  Understanding Deep Neural Networks  Southampton  35 hours  Mon, 20180212 09:30  £5500 / £6750  
mlentre  Machine Learning Concepts for Entrepreneurs and Managers  Southampton  21 hours  Mon, 20180212 09:30  £3300 / £4050  
mlios  Machine Learning on iOS  Southampton  14 hours  Tue, 20180213 09:30  £2200 / £2700  
Fairseq  Fairseq: Setting up a CNNbased machine translation system  Southampton  7 hours  Wed, 20180214 09:30  £1100 / £1350  
snorkel  Snorkel: Rapidly process training data  Southampton  7 hours  Fri, 20180216 09:30  £1100 / £1350  
annmldt  Artificial Neural Networks, Machine Learning, Deep Thinking  Southampton  21 hours  Mon, 20180226 09:30  £3900 / £4650  
mldt  Machine Learning and Deep Learning  Southampton  21 hours  Mon, 20180226 09:30  £3900 / £4650  
systemml  Apache SystemML for Machine Learning  Southampton  14 hours  Mon, 20180226 09:30  £2200 / £2700  
pythonadvml  Python for Advanced Machine Learning  Southampton  21 hours  Mon, 20180226 09:30  £3300 / £4050  
aiintrozero  From Zero to AI  Southampton  35 hours  Mon, 20180226 09:30  £6500 / £7750  
BigData_  A practical introduction to Data Analysis and Big Data  Southampton  35 hours  Tue, 20180227 09:30  £5500 / £6500  
bspkannmldt  Artificial Neural Networks, Machine Learning and Deep Thinking  Southampton  21 hours  Tue, 20180227 09:30  £3300 / £4050  
Fairsec  Fairsec: Setting up a CNNbased machine translation system  Southampton  7 hours  Wed, 20180228 09:30  £1100 / £1350  
predio  Machine Learning with PredictionIO  Southampton  21 hours  Wed, 20180228 09:30  £3300 / £4050  
matlabml1  Introduction to Machine Learning with MATLAB  Southampton  21 hours  Wed, 20180228 09:30  £3300 / £4050  
octnp  Octave not only for programmers  Southampton  21 hours  Wed, 20180228 09:30  £3300 / £4050  
facebooknmt  Facebook NMT: Setting up a Neural Machine Translation System  Southampton  7 hours  Thu, 20180301 09:30  £1100 / £1350  
dmmlr  Data Mining & Machine Learning with R  Southampton  14 hours  Thu, 20180301 09:30  £2600 / £3100  
dladv  Advanced Deep Learning  Southampton  28 hours  Tue, 20180306 09:30  £5200 / £6200  
encogintro  Encog: Introduction to Machine Learning  Southampton  14 hours  Wed, 20180307 09:30  £2200 / £2700  
textsum  Text Summarization with Python  Southampton  14 hours  Wed, 20180307 09:30  £2200 / £2700  
wolfdata  Data Science: Analysis and Presentation  Southampton  7 hours  Fri, 20180309 09:30  £1100 / £1350  
Torch  Torch: Getting started with Machine and Deep Learning  Southampton  21 hours  Mon, 20180312 09:30  £3900 / £4650  
opennmt  OpenNMT: Setting up a Neural Machine Translation System  Southampton  7 hours  Mon, 20180312 09:30  £1100 / £1350  
mlintro  Introduction to Machine Learning  Southampton  7 hours  Mon, 20180312 09:30  £1300 / £1550  
pythontextml  Python: Machine Learning with Text  Southampton  21 hours  Tue, 20180313 09:30  £3300 / £3800  
cpde  Data Engineering on Google Cloud Platform  Southampton  32 hours  Tue, 20180313 09:30  £5500 / £6500  
mlfunpython  Machine Learning Fundamentals with Python  Southampton  14 hours  Tue, 20180313 09:30  £2200 / £2700  
appliedml  Applied Machine Learning  Southampton  14 hours  Wed, 20180314 09:30  £2600 / £3100  
mlbankingr  Machine Learning for Banking (with R)  Southampton  28 hours  Tue, 20180320 09:30  £4400 / £5400  
patternmatching  Pattern Matching  Southampton  14 hours  Wed, 20180321 09:30  £2600 / £3100  
mlfsas  Machine Learning Fundamentals with Scala and Apache Spark  Southampton  14 hours  Wed, 20180321 09:30  £2200 / £2700  
OpenNN  OpenNN: Implementing neural networks  Southampton  14 hours  Wed, 20180321 09:30  £2600 / £3100  
MLFWR1  Machine Learning Fundamentals with R  Southampton  14 hours  Wed, 20180321 09:30  £2600 / £3100  
radvml  Advanced Machine Learning with R  Southampton  21 hours  Mon, 20180326 09:30  £3900 / £4650  
matlabdl  Matlab for Deep Learning  Southampton  14 hours  Mon, 20180326 09:30  £2200 / £2700  
Neuralnettf  Neural Networks Fundamentals using TensorFlow as Example  Southampton  28 hours  Tue, 20180327 09:30  £5200 / £6200  
datamodeling  Pattern Recognition  Southampton  35 hours  Mon, 20180402 09:30  £6500 / £7750  
aiauto  Artificial Intelligence in Automotive  Southampton  14 hours  Tue, 20180403 09:30  £2600 / £3100  
mlrobot1  Machine Learning for Robotics  Southampton  21 hours  Wed, 20180404 09:30  £3300 / £4050  
mlios  Machine Learning on iOS  Southampton  14 hours  Wed, 20180404 09:30  £2200 / £2700  
Fairseq  Fairseq: Setting up a CNNbased machine translation system  Southampton  7 hours  Thu, 20180405 09:30  £1100 / £1350  
mlentre  Machine Learning Concepts for Entrepreneurs and Managers  Southampton  21 hours  Mon, 20180409 09:30  £3300 / £4050  
snorkel  Snorkel: Rapidly process training data  Southampton  7 hours  Thu, 20180419 09:30  £1100 / £1350  
facebooknmt  Facebook NMT: Setting up a Neural Machine Translation System  Southampton  7 hours  Fri, 20180420 09:30  £1100 / £1350  
dmmlr  Data Mining & Machine Learning with R  Southampton  14 hours  Mon, 20180423 09:30  £2600 / £3100  
mldt  Machine Learning and Deep Learning  Southampton  21 hours  Mon, 20180423 09:30  £3900 / £4650  
undnn  Understanding Deep Neural Networks  Southampton  35 hours  Mon, 20180423 09:30  £5500 / £6750  
bspkannmldt  Artificial Neural Networks, Machine Learning and Deep Thinking  Southampton  21 hours  Mon, 20180423 09:30  £3300 / £4050  
annmldt  Artificial Neural Networks, Machine Learning, Deep Thinking  Southampton  21 hours  Tue, 20180424 09:30  £3900 / £4650  
Fairsec  Fairsec: Setting up a CNNbased machine translation system  Southampton  7 hours  Tue, 20180424 09:30  £1100 / £1350  
predio  Machine Learning with PredictionIO  Southampton  21 hours  Tue, 20180424 09:30  £3300 / £4050  
BigData_  A practical introduction to Data Analysis and Big Data  Southampton  35 hours  Tue, 20180424 09:30  £5500 / £6500  
matlabml1  Introduction to Machine Learning with MATLAB  Southampton  21 hours  Tue, 20180424 09:30  £3300 / £4050  
octnp  Octave not only for programmers  Southampton  21 hours  Tue, 20180424 09:30  £3300 / £4050  
pythonadvml  Python for Advanced Machine Learning  Southampton  21 hours  Wed, 20180425 09:30  £3300 / £4050  
opennlp  OpenNLP for Text Based Machine Learning  Southampton  14 hours  Thu, 20180426 09:30  £2200 / £2700  
encogadv  Encog: Advanced Machine Learning  Southampton  14 hours  Mon, 20180430 09:30  £2200 / £2700  
mlintro  Introduction to Machine Learning  Southampton  7 hours  Mon, 20180430 09:30  £1300 / £1550  
wolfdata  Data Science: Analysis and Presentation  Southampton  7 hours  Mon, 20180430 09:30  £1100 / £1350  
aiintrozero  From Zero to AI  Southampton  35 hours  Mon, 20180430 09:30  £6500 / £7750  
dladv  Advanced Deep Learning  Southampton  28 hours  Mon, 20180430 09:30  £5200 / £6200  
systemml  Apache SystemML for Machine Learning  Southampton  14 hours  Tue, 20180501 09:30  £2200 / £2700  
textsum  Text Summarization with Python  Southampton  14 hours  Tue, 20180501 09:30  £2200 / £2700  
opennmt  OpenNMT: Setting up a Neural Machine Translation System  Southampton  7 hours  Wed, 20180502 09:30  £1100 / £1350  
mlfunpython  Machine Learning Fundamentals with Python  Southampton  14 hours  Wed, 20180502 09:30  £2200 / £2700  
appliedml  Applied Machine Learning  Southampton  14 hours  Thu, 20180503 09:30  £2600 / £3100  
cpde  Data Engineering on Google Cloud Platform  Southampton  32 hours  Tue, 20180508 09:30  £5500 / £6500  
Torch  Torch: Getting started with Machine and Deep Learning  Southampton  21 hours  Wed, 20180509 09:30  £3900 / £4650  
patternmatching  Pattern Matching  Southampton  14 hours  Thu, 20180510 09:30  £2600 / £3100  
mlfsas  Machine Learning Fundamentals with Scala and Apache Spark  Southampton  14 hours  Thu, 20180510 09:30  £2200 / £2700  
OpenNN  OpenNN: Implementing neural networks  Southampton  14 hours  Thu, 20180510 09:30  £2600 / £3100  
dsstne  Amazon DSSTNE: Build a recommendation system  Southampton  7 hours  Thu, 20180510 09:30  £1100 / £1350  
MLFWR1  Machine Learning Fundamentals with R  Southampton  14 hours  Thu, 20180510 09:30  £2600 / £3100  
mlbankingr  Machine Learning for Banking (with R)  Southampton  28 hours  Mon, 20180514 09:30  £4400 / £5400  
pythontextml  Python: Machine Learning with Text  Southampton  21 hours  Tue, 20180515 09:30  £3300 / £3800  
encogintro  Encog: Introduction to Machine Learning  Southampton  14 hours  Wed, 20180516 09:30  £2200 / £2700  
matlabdl  Matlab for Deep Learning  Southampton  14 hours  Wed, 20180516 09:30  £2200 / £2700  
radvml  Advanced Machine Learning with R  Southampton  21 hours  Wed, 20180516 09:30  £3900 / £4650  
Neuralnettf  Neural Networks Fundamentals using TensorFlow as Example  Southampton  28 hours  Mon, 20180521 09:30  £5200 / £6200  
mlbankingpython_  Machine Learning for Banking (with Python)  Southampton  21 hours  Tue, 20180522 09:30  £3300 / £4050  
aiauto  Artificial Intelligence in Automotive  Southampton  14 hours  Wed, 20180523 09:30  £2600 / £3100  
Fairseq  Fairseq: Setting up a CNNbased machine translation system  Southampton  7 hours  Thu, 20180524 09:30  £1100 / £1350  
mlrobot1  Machine Learning for Robotics  Southampton  21 hours  Tue, 20180529 09:30  £3300 / £4050  
mlentre  Machine Learning Concepts for Entrepreneurs and Managers  Southampton  21 hours  Wed, 20180530 09:30  £3300 / £4050  
mlios  Machine Learning on iOS  Southampton  14 hours  Thu, 20180531 09:30  £2200 / £2700  
datamodeling  Pattern Recognition  Southampton  35 hours  Mon, 20180604 09:30  £6500 / £7750  
facebooknmt  Facebook NMT: Setting up a Neural Machine Translation System  Southampton  7 hours  Fri, 20180608 09:30  £1100 / £1350  
snorkel  Snorkel: Rapidly process training data  Southampton  7 hours  Fri, 20180608 09:30  £1100 / £1350  
Fairsec  Fairsec: Setting up a CNNbased machine translation system  Southampton  7 hours  Tue, 20180612 09:30  £1100 / £1350  
mldt  Machine Learning and Deep Learning  Southampton  21 hours  Wed, 20180613 09:30  £3900 / £4650 
Code  Name  Duration  Outline 

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
Developing PredictionIO
Machine Learning Education and Usage Examples
PredictionIO SDKs (Select One)

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
Format of the course Introduction Overview of the Torch project Overview of a Convolutional Neural Machine Translation model Overview of training approaches 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:
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
Basic Concepts of a Neural Network (Application: multilayer perceptron)
Standard ML / DL Tools A simple presentation with advantages, disadvantages, position in the ecosystem and use is planned.
Convolutional Neural Networks (CNN).
Recurrent Neural Networks (RNN).
Deep Reinforcement Learning.
Part 2 – Theano for Deep Learning Theano Basics
Theano Functions
Training and Optimization of a neural network using Theano
Testing the model
TensorFlow Basics
TensorFlow Mechanics
The Perceptron
From the Perceptron to Support Vector Machines
Artificial Neural Networks
Convolutional Neural Networks
Basic Introductions to be given to the below modules(Brief Introduction to be provided based on time availability): Tensorflow  Advanced Usage

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. AudienceThis course is suitable for Machine Learning researchers, developers and engineers seeking to utilize SystemML as a framework for machine learning. Running SystemML
Tools
Languages and ML Algorithms

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:
This class is intended for experienced developers who are responsible for managing big data transformations including:
The course includes presentations, demonstrations, and handson labs. Leveraging Unstructured Data with Cloud Dataproc on Google Cloud PlatformModule 1: Google Cloud Dataproc Overview
Module 2: Running Dataproc Jobs
Module 3: Integrating Dataproc with Google Cloud Platform
Module 4: Making Sense of Unstructured Data with Google’s Machine Learning APIs
Serverless Data Analysis with Google BigQuery and Cloud DataflowModule 5: Serverless data analysis with BigQuery
Module 6: Serverless, autoscaling data pipelines with Dataflow
Serverless Machine Learning with TensorFlow on Google Cloud PlatformModule 7: Getting started with Machine Learning
Module 8: Building ML models with Tensorflow
Module 9: Scaling ML models with CloudML
Module 10: Feature Engineering
Building Resilient Streaming Systems on Google Cloud PlatformModule 11: Architecture of streaming analytics pipelines
Module 12: Ingesting Variable Volumes
Module 13: Implementing streaming pipelines
Module 14: Streaming analytics and dashboards
Module 15: High throughput and lowlatency with 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:
Audience
Format of the course
Introduction to Text Summarization with Python
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+
Summarization
Creating a Python application using pysummarization library on Python 2.7/3.3+
Creating a Python application using readless library on Python 2.7/3.3+
Creating simple Python test code that uses readless library to generate a text summary Troubleshooting and debugging Closing Remarks 
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)
Statistics (10.5h)
Intro to programming (3.5h)
Machine Learning (10.5h)
Rules Engines and Expert Systems (7 hours)

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. Audience
Format of the course Note
Introduction Overview of the Torch project Overview of a Convolutional Neural Machine Translation model Overview of training approaches 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 
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. AudienceData 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.

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
Rules based AI
Machine Learning
Deep Learning
Deep Learning in practice (mainly using TensorFlow)
Sample usage

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
Format of the course
Note
Introduction Overview of the Torch and Caffe2 projects Overview of a Convolutional Neural Machine Translation model Overview of training approaches 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 
appliedml  Applied Machine Learning  14 hours 
This training course is for people that would like to apply Machine Learning in practical applications. AudienceThis 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.

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
TensorFlow Mechanics
The Perceptron
From the Perceptron to Support Vector Machines
Artificial Neural Networks
Convolutional Neural Networks

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:
Audience
Format of the course
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
Regression
Classification
Crossvalidation and Resampling
Unsupervised Learning

mldt  Machine Learning and Deep Learning  21 hours 
This course covers AI (emphasizing Machine Learning and Deep Learning) Machine learningIntroduction to Machine Learning
Regression
Resampling Methods
Model Selection and Regularization
Classification
Introduction to Deep LearningANN Structure
Feed forward ANN.
Deep Learning
Lab:Getting Started with R
Regression
Classification
Note:

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:
Audience
Format of the course
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
Machine Learning with Python
Regression
Classification
Crossvalidation and Resampling
Unsupervised Learning

deepmclrg  Machine Learning & Deep Learning with Python and R  14 hours 
MACHINE LEARNING1: Introducing Machine Learning
2: Managing and Understanding Data
3: Lazy Learning – Classification Using Nearest Neighbors
4: Probabilistic Learning – Classification Using
5: Divide and Conquer – Classification Using
6: Forecasting Numeric Data – Regression Methods
7: Black Box Methods – Neural Networks and
8: Finding Patterns – Market Basket Analysis Using
9: Finding Groups of Data – Clustering with kmeans
10: Evaluating Model Performance
11: Improving Model Performance
DEEP LEARNING with R1: Getting Started with Deep Learning
2: Training a Prediction Model
3: Preventing Overfitting
4: Identifying Anomalous Data
5: Training Deep Prediction Models
6: Tuning and Optimizing Models
DEEP LEARNING WITH PYTHONI Introduction1 Welcome
II Background2 Introduction to Theano
3 Introduction to TensorFlow
4 Introduction to Keras
5 Project: Develop Large Models on GPUs Cheaply In the Cloud
III Multilayer Perceptrons6 Crash Course In Multilayer Perceptrons
7 Develop Your First Neural Network With Keras
8 Evaluate The Performance of Deep Learning Models
9 Use Keras Models With ScikitLearn For General Machine Learning
10 Project: Multiclass Classification Of Flower Species
11 Project: Binary Classification Of Sonar Returns
12 Project: Regression Of Boston House Prices
IV Advanced Multilayer Perceptrons and Keras13 Save Your Models For Later With Serialization
14 Keep The Best Models During Training With Checkpointing
15 Understand Model Behavior During Training By Plotting History
16 Reduce Overfitting With Dropout Regularization
17 Lift Performance With Learning Rate Schedules
V Convolutional Neural Networks18 Crash Course In Convolutional Neural Networks
19 Project: Handwritten Digit Recognition
20 Improve Model Performance With Image Augmentation
21 Project Object Recognition in Photographs
22 Project: Predict Sentiment From Movie Reviews
VI Recurrent Neural Networks23 Crash Course In Recurrent Neural Networks
24 Time Series Prediction with Multilayer Perceptrons
25 Time Series Prediction with LSTM Recurrent Neural Networks
26 Project: Sequence Classification of Movie Reviews
27 Understanding Stateful LSTM Recurrent Neural Networks
28 Project: Text Generation With Alice in Wonderland

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:
Audience
Format of the course
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 NETWORKSIntroduction and ANN Structure.
Mathematical Foundations and Learning mechanisms.
Single layer perceptrons.
Feedforward ANN.
Radial Basis Function Networks.
Competitive Learning and Self organizing ANN.
Fuzzy Neural Networks.
Applications
DAY 2 MACHINE LEARNING
DAY 3  DEEP LEARNINGThis will be taught in relation to the topics covered on Day 1 and Day 2

wolfdata  Data Science: Analysis and Presentation  7 hours 
The Wolfram System's integrated environment makes it an efficient tool for both analyzing and presenting data. This course covers aspects of the Wolfram Language relevant to analytics, including statistical computation, visualization, data import and export and automatic generation of reports.

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:
Audience
Format of the course
To request a customized course outline for this training, please contact us. 
bspkaml  Machine Learning  21 hours 
This course will be a combination of theory and practical work with specific examples used throughout the event.
IntroductionThis section provides a general introduction of when to use 'machine learning', what should be considered and what it all means including the pros and cons. Datatypes (structured/unstructured/static/streamed), data validity/volume, data driven vs user driven analytics, statistical models vs. machine learning models/ challenges of unsupervised learning, biasvariance trade off, iteration/evaluation, crossvalidation approaches, supervised/unsupervised/reinforcement. MAJOR TOPICS1.Understanding naive Bayes
2.Understanding decision trees
3. Understanding neural networks
4. Understanding Support Vector Machines
5. Understanding clustering
6. Measuring performance for classification
7. Tuning stock models for better performance
MINOR TOPICS8. Understanding classification using nearest neighbors
9. Understanding classification rules
10.Understanding regression
11.Understanding regression trees and model trees
12. Understanding association rules
Extras

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
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

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:
Audience
Format of the course
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.

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 Format of the course Introduction Alignment Gauging Inspection Closing remarks

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:
Audience
Format of the course
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.

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 Format of the course Introduction to Torch Installing Torch Installing Torch packages Choosing an IDE for Torch Working with the Lua scripting language and LuaJIT Loading a dataset in Torch Machine Learning in Torch Image analysis with Torch Working with the REPL interpreter Working with databases Networking and Torch GPU support in Torch Integrating Torch Embedding Torch Other frameworks and libraries Creating your own package Testing and debugging Releasing your application The future of AI and Torch 
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:
Audience
Format of the course
Introduction
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 
bspkannmldt  Artificial Neural Networks, Machine Learning and Deep Thinking  21 hours 
1. Understanding classification using nearest neighbors
2.Understanding naive Bayes
3.Understanding decision trees
4. Understanding classification rules
5.Understanding regression
6.Understanding regression trees and model trees
7. Understanding neural networks
8. Understanding Support Vector Machines
9. Understanding association rules
10. Understanding clustering
11. Measuring performance for classification
12. Tuning stock models for better performance
13. Deep Learning
14. Discussion of Specific Application Areas 
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 Format of the course Introduction to OpenNN, Machine Learning and Deep Learning Downloading OpenNN Working with Neural Designer OpenNN architecture OpenNN classes Building a neural network application Working with datasets Learning tasks Compiling with QT Creator Integrating, testing and debugging your application The future of neural networks and OpenNN 
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:
Audience
Format of the course
To request a customized course outline for this training, please contact us. 
dladv  Advanced Deep Learning  28 hours 

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
Format of the course
Introduction to Data Analysis and Big Data
Languages used for Data Analysis
Approaches to Data Analysis
Big Data infrastructure

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:
Audience
Format of the course
To request a customized course outline for this training, please contact us. 
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
Machine Learning with Python
Regression
Classification
Crossvalidation and Resampling
Unsupervised Learning

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. IntroductionSimple calculations
The Octave environment
Arrays and vectors
Plotting graphs
Octave programming I: Script files
Control statements
Octave programming II: FunctionsMatrices and vectors
Linear and Nonlinear EquationsMore graphs
Eigenvectors and the Singular Value DecompositionComplex numbers
Statistics and data processingGUI 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
Format of the course
Introduction
Different Types of Machine Learning
Machine Learning Languages and Toolsets
Machine Learning Case Studies
Introduction to R
How to Load Machine Learning Data
Modeling Business Decisions with Supervised Learning
Regression Analysis
Classification
Handson: Building an Estimation Model
Evaluating the performance of Machine Learning Algorithms
Modeling Business Decisions with Unsupervised Learning
Handson: Building a Recommendation System
Extending your company's capabilities
Closing Remarks 
dmmlr  Data Mining & Machine Learning with R  14 hours 
Introduction to Data mining and Machine Learning
Regression
Classification
Crossvalidation and Resampling
Unsupervised Learning
Advanced topics
Multidimensional reduction

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
Introduction to Neural NetworksIntroduction to Applied Machine Learning
Machine Learning with Python
Machine learning Concepts and ApplicationsRegression
Classification
Crossvalidation and Resampling
Unsupervised Learning
Short Introduction to NLP methods
Artificial Intelligence & Deep LearningTechnical Overview

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
Format of the course
Introduction
Different Types of Machine Learning
Machine Learning Languages and Toolsets
Machine Learning Case Studies
Handson: Python for Machine Learning
How to Load Machine Learning Data
Modeling Business Decisions with Supervised Learning
Regression Analysis
Classification
Handson: Building an Estimation Model
Evaluating the performance of Machine Learning Algorithms
Modeling Business Decisions with Unsupervised Learning
Handson: Building a Recommendation System
Extending your company's capabilities
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:
This class is intended for the following:
The course includes presentations, demonstrations, and handson labs.Module 1: Introducing Google Cloud Platform
Module 2: Compute and Storage Fundamentals
Module 3: Data Analytics on the Cloud
Module 4: Scaling Data Analysis
Module 5: Data Processing Architectures
Module 6: Summary

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
Format of the course
Introduction 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:
Audience
Format of the course
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 