Machine Learning Training in Portsmouth

Machine Learning Training in Portsmouth

Machine Learning courses

Portsmouth

Building 1000, Lakeside North Harbour, Western Road
Portsmouth, HAM PO6 3EZ
United Kingdom
Hampshire GB
Portsmouth
The training rooms are located within a 20 minute walk from Cosham Railway station. A taxi service from the station is also available. While you are enjoying...Read more

Client Testimonials

Machine Learning and Deep Learning

We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company

Sebastiaan Holman - Travix International

Applied Machine Learning

ref material to use later was very good

PAUL BEALES - Seagate Technology

Neural Networks Fundamentals using TensorFlow as Example

Topic selection. Style of training. Practice orientation

Commerzbank AG

Neural Networks Fundamentals using TensorFlow as Example

Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for

Commerzbank AG

Artificial Neural Networks, Machine Learning and Deep Thinking

flexibility

Werner Philipp - Robert Bosch GmbH

Artificial Neural Networks, Machine Learning and Deep Thinking

Very flexible

Frank Ueltzhöffer - Robert Bosch GmbH

Advanced Deep Learning

Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.

Paul Kassis - OSONES

Neural Networks Fundamentals using TensorFlow as Example

Topic selection. Style of training. Practice orientation

Commerzbank AG

A practical introduction to Data Analysis and Big Data

presentation of technologies

Continental AG / Abteilung: CF IT Finance

Neural Networks Fundamentals using TensorFlow as Example

I liked the opportunities to ask questions and get more in depth explanations of the theory.

Sharon Ruane - INTEL R&D IRELAND LIMITED

Neural Networks Fundamentals using TensorFlow as Example

I was amazed at the standard of this class - I would say that it was university standard.

David Relihan - INTEL R&D IRELAND LIMITED

Artificial Neural Networks, Machine Learning and Deep Thinking

flexibility

Werner Philipp - Robert Bosch GmbH

A practical introduction to Data Analysis and Big Data

It covered a broad range of information.

Continental AG / Abteilung: CF IT Finance

A practical introduction to Data Analysis and Big Data

Willingness to share more

Balaram Chandra Paul - MOL Information Technology Asia Limited

Data Mining & Machine Learning with R

The trainer was so knowledgeable and included areas I was interested in

Mohamed Salama - Edmonton Police Service

A practical introduction to Data Analysis and Big Data

Overall the Content was good.

Sameer Rohadia - Continental AG / Abteilung: CF IT Finance

Artificial Neural Networks, Machine Learning, Deep Thinking

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease - Knowledgepool Group Ltd

Artificial Neural Networks, Machine Learning and Deep Thinking

flexibility

Werner Philipp - Robert Bosch GmbH

Machine Learning and Deep Learning

Coverage and depth of topics

Anirban Basu - Travix International

Advanced Deep Learning

The global overview of deep learning

Bruno Charbonnier - OSONES

Machine Learning and Deep Learning

The training provided the right foundation that allows us to further to expand on, by showing how theory and practice go hand in hand. It actually got me more interested in the subject than I was before.

Jean-Paul van Tillo - Travix International

Neural Networks Fundamentals using TensorFlow as Example

Very good all round overview.Good background into why Tensorflow operates as it does.

Kieran Conboy - INTEL R&D IRELAND LIMITED

Neural Networks Fundamentals using TensorFlow as Example

Knowledgeable trainer

Sridhar Voorakkara - INTEL R&D IRELAND LIMITED

Advanced Deep Learning

The exercises are sufficiently practical and do not need a high knowledge in Python to be done.

Alexandre GIRARD - OSONES

Artificial Neural Networks, Machine Learning and Deep Thinking

flexibility

Werner Philipp - Robert Bosch GmbH

Machine Learning Course Events - Portsmouth

Code Name Venue Duration Course Date PHP Course Price [Remote / Classroom]
mlfsas Machine Learning Fundamentals with Scala and Apache Spark Portsmouth 14 hours Thu, 2018-02-01 09:30 £2200 / £2500
opennlp OpenNLP for Text Based Machine Learning Portsmouth Technopole 14 hours Thu, 2018-02-01 09:30 £2200 / £2500
dsstne Amazon DSSTNE: Build a recommendation system Portsmouth Technopole 7 hours Fri, 2018-02-02 09:30 £1100 / £1250
opennmt OpenNMT: Setting up a Neural Machine Translation System Portsmouth Technopole 7 hours Fri, 2018-02-02 09:30 £1100 / £1250
cpde Data Engineering on Google Cloud Platform Portsmouth Technopole 32 hours Mon, 2018-02-05 09:30 £5500 / £6100
facebooknmt Facebook NMT: Setting up a Neural Machine Translation System Portsmouth Technopole 7 hours Mon, 2018-02-05 09:30 £1100 / £1250
matlabml1 Introduction to Machine Learning with MATLAB Portsmouth 21 hours Mon, 2018-02-05 09:30 £3300 / £3750
datamodeling Pattern Recognition Portsmouth Technopole 35 hours Mon, 2018-02-05 09:30 £6500 / £7250
cpde Data Engineering on Google Cloud Platform Portsmouth 32 hours Tue, 2018-02-06 09:30 £5500 / £6100
pythonadvml Python for Advanced Machine Learning Portsmouth 21 hours Tue, 2018-02-06 09:30 £3300 / £3750
Fairsec Fairsec: Setting up a CNN-based machine translation system Portsmouth Technopole 7 hours Tue, 2018-02-06 09:30 £1100 / £1250
Torch Torch: Getting started with Machine and Deep Learning Portsmouth Technopole 21 hours Wed, 2018-02-07 09:30 £3900 / £4350
mlintro Introduction to Machine Learning Portsmouth Technopole 7 hours Wed, 2018-02-07 09:30 £1300 / £1450
mlfunpython Machine Learning Fundamentals with Python Portsmouth Technopole 14 hours Wed, 2018-02-07 09:30 £2200 / £2500
mlfsas Machine Learning Fundamentals with Scala and Apache Spark Portsmouth Technopole 14 hours Mon, 2018-02-12 09:30 £2200 / £2500
aiauto Artificial Intelligence in Automotive Portsmouth Technopole 14 hours Mon, 2018-02-12 09:30 £2600 / £2900
octnp Octave not only for programmers Portsmouth Technopole 21 hours Mon, 2018-02-12 09:30 £3300 / £3750
bspkannmldt Artificial Neural Networks, Machine Learning and Deep Thinking Portsmouth 21 hours Tue, 2018-02-13 09:30 £3300 / £3750
mlbankingpython_ Machine Learning for Banking (with Python) Portsmouth 21 hours Tue, 2018-02-13 09:30 £3300 / £3750
appliedml Applied Machine Learning Portsmouth Technopole 14 hours Tue, 2018-02-13 09:30 £2600 / £2900
wolfdata Data Science: Analysis and Presentation Portsmouth 7 hours Wed, 2018-02-14 09:30 £1100 / £1250
OpenNN OpenNN: Implementing neural networks Portsmouth Technopole 14 hours Wed, 2018-02-14 09:30 £2600 / £2900
mlios Machine Learning on iOS Portsmouth Technopole 14 hours Thu, 2018-02-15 09:30 £2200 / £2500
mlbankingpython_ Machine Learning for Banking (with Python) Portsmouth Technopole 21 hours Mon, 2018-02-19 09:30 £3300 / £3750
MLFWR1 Machine Learning Fundamentals with R Portsmouth Technopole 14 hours Mon, 2018-02-19 09:30 £2600 / £2900
dmmlr Data Mining & Machine Learning with R Portsmouth 14 hours Mon, 2018-02-19 09:30 £2600 / £2900
OpenNN OpenNN: Implementing neural networks Portsmouth 14 hours Mon, 2018-02-19 09:30 £2600 / £2900
undnn Understanding Deep Neural Networks Portsmouth 35 hours Mon, 2018-02-19 09:30 £5500 / £6250
snorkel Snorkel: Rapidly process training data Portsmouth 7 hours Mon, 2018-02-19 09:30 £1100 / £1250
predio Machine Learning with PredictionIO Portsmouth 21 hours Mon, 2018-02-19 09:30 £3300 / £3750
MLFWR1 Machine Learning Fundamentals with R Portsmouth 14 hours Tue, 2018-02-20 09:30 £2600 / £2900
Fairsec Fairsec: Setting up a CNN-based machine translation system Portsmouth 7 hours Tue, 2018-02-20 09:30 £1100 / £1250
textsum Text Summarization with Python Portsmouth 14 hours Tue, 2018-02-20 09:30 £2200 / £2500
facebooknmt Facebook NMT: Setting up a Neural Machine Translation System Portsmouth 7 hours Wed, 2018-02-21 09:30 £1100 / £1250
aiauto Artificial Intelligence in Automotive Portsmouth 14 hours Wed, 2018-02-21 09:30 £2600 / £2900
mlrobot1 Machine Learning for Robotics Portsmouth Technopole 21 hours Wed, 2018-02-21 09:30 £3300 / £3750
pythonadvml Python for Advanced Machine Learning Portsmouth Technopole 21 hours Wed, 2018-02-21 09:30 £3300 / £3750
radvml Advanced Machine Learning with R Portsmouth Technopole 21 hours Wed, 2018-02-21 09:30 £3900 / £4350
patternmatching Pattern Matching Portsmouth Technopole 14 hours Thu, 2018-02-22 09:30 £2600 / £2900
mlfunpython Machine Learning Fundamentals with Python Portsmouth 14 hours Thu, 2018-02-22 09:30 £2200 / £2500
BigData_ A practical introduction to Data Analysis and Big Data Portsmouth 35 hours Mon, 2018-02-26 09:30 £5500 / £6100
BigData_ A practical introduction to Data Analysis and Big Data Portsmouth Technopole 35 hours Mon, 2018-02-26 09:30 £5500 / £6250
undnn Understanding Deep Neural Networks Portsmouth Technopole 35 hours Mon, 2018-02-26 09:30 £5500 / £6250
Fairseq Fairseq: Setting up a CNN-based machine translation system Portsmouth 7 hours Mon, 2018-02-26 09:30 £1100 / £1250
opennlp OpenNLP for Text Based Machine Learning Portsmouth 14 hours Mon, 2018-02-26 09:30 £2200 / £2500
annmldt Artificial Neural Networks, Machine Learning, Deep Thinking Portsmouth 21 hours Tue, 2018-02-27 09:30 £3900 / £4350
mlrobot1 Machine Learning for Robotics Portsmouth 21 hours Tue, 2018-02-27 09:30 £3300 / £3750
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Portsmouth Technopole 28 hours Tue, 2018-02-27 09:30 £5200 / £5800
mlentre Machine Learning Concepts for Entrepreneurs and Managers Portsmouth Technopole 21 hours Wed, 2018-02-28 09:30 £3300 / £3750
Fairseq Fairseq: Setting up a CNN-based machine translation system Portsmouth Technopole 7 hours Wed, 2018-02-28 09:30 £1100 / £1250
patternmatching Pattern Matching Portsmouth 14 hours Wed, 2018-02-28 09:30 £2600 / £2900
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Portsmouth 28 hours Mon, 2018-03-05 09:30 £5200 / £5800
aiintrozero From Zero to AI Portsmouth 35 hours Mon, 2018-03-05 09:30 £6500 / £7250
dladv Advanced Deep Learning Portsmouth Technopole 28 hours Mon, 2018-03-05 09:30 £5200 / £5800
aiintrozero From Zero to AI Portsmouth Technopole 35 hours Mon, 2018-03-05 09:30 £6500 / £7250
mlintro Introduction to Machine Learning Portsmouth 7 hours Tue, 2018-03-06 09:30 £1300 / £1450
wolfdata Data Science: Analysis and Presentation Portsmouth Technopole 7 hours Tue, 2018-03-06 09:30 £1100 / £1250
dladv Advanced Deep Learning Portsmouth 28 hours Tue, 2018-03-06 09:30 £5200 / £5800
matlabdl Matlab for Deep Learning Portsmouth Technopole 14 hours Tue, 2018-03-06 09:30 £2200 / £2500
mlentre Machine Learning Concepts for Entrepreneurs and Managers Portsmouth 21 hours Tue, 2018-03-06 09:30 £3300 / £3750
matlabml1 Introduction to Machine Learning with MATLAB Portsmouth Technopole 21 hours Wed, 2018-03-07 09:30 £3300 / £3750
pythontextml Python: Machine Learning with Text Portsmouth Technopole 21 hours Wed, 2018-03-07 09:30 £3300 / £3600
encogadv Encog: Advanced Machine Learning Portsmouth Technopole 14 hours Wed, 2018-03-07 09:30 £2200 / £2500
radvml Advanced Machine Learning with R Portsmouth 21 hours Wed, 2018-03-07 09:30 £3900 / £4350
opennmt OpenNMT: Setting up a Neural Machine Translation System Portsmouth 7 hours Fri, 2018-03-09 09:30 £1100 / £1250
snorkel Snorkel: Rapidly process training data Portsmouth Technopole 7 hours Fri, 2018-03-09 09:30 £1100 / £1250
mldt Machine Learning and Deep Learning Portsmouth Technopole 21 hours Mon, 2018-03-12 09:30 £3900 / £4350
mlios Machine Learning on iOS Portsmouth 14 hours Mon, 2018-03-12 09:30 £2200 / £2500
appliedml Applied Machine Learning Portsmouth 14 hours Mon, 2018-03-12 09:30 £2600 / £2900
pythontextml Python: Machine Learning with Text Portsmouth 21 hours Tue, 2018-03-13 09:30 £3300 / £3600
Torch Torch: Getting started with Machine and Deep Learning Portsmouth 21 hours Wed, 2018-03-14 09:30 £3900 / £4350
octnp Octave not only for programmers Portsmouth 21 hours Wed, 2018-03-14 09:30 £3300 / £3750
dmmlr Data Mining & Machine Learning with R Portsmouth Technopole 14 hours Wed, 2018-03-14 09:30 £2600 / £2900
matlabdl Matlab for Deep Learning Portsmouth 14 hours Thu, 2018-03-15 09:30 £2200 / £2500
textsum Text Summarization with Python Portsmouth Technopole 14 hours Mon, 2018-03-19 09:30 £2200 / £2500
mldt Machine Learning and Deep Learning Portsmouth 21 hours Mon, 2018-03-19 09:30 £3900 / £4350
annmldt Artificial Neural Networks, Machine Learning, Deep Thinking Portsmouth Technopole 21 hours Tue, 2018-03-20 09:30 £3900 / £4350
encogadv Encog: Advanced Machine Learning Portsmouth 14 hours Tue, 2018-03-20 09:30 £2200 / £2500
systemml Apache SystemML for Machine Learning Portsmouth Technopole 14 hours Tue, 2018-03-20 09:30 £2200 / £2500
encogintro Encog: Introduction to Machine Learning Portsmouth 14 hours Wed, 2018-03-21 09:30 £2200 / £2500
dsstne Amazon DSSTNE: Build a recommendation system Portsmouth 7 hours Fri, 2018-03-23 09:30 £1100 / £1250
mlfsas Machine Learning Fundamentals with Scala and Apache Spark Portsmouth 14 hours Mon, 2018-03-26 09:30 £2200 / £2500
mlbankingr Machine Learning for Banking (with R) Portsmouth Technopole 28 hours Mon, 2018-03-26 09:30 £4400 / £5000
systemml Apache SystemML for Machine Learning Portsmouth 14 hours Mon, 2018-03-26 09:30 £2200 / £2500
datamodeling Pattern Recognition Portsmouth 35 hours Mon, 2018-03-26 09:30 £6500 / £7250
encogintro Encog: Introduction to Machine Learning Portsmouth Technopole 14 hours Tue, 2018-03-27 09:30 £2200 / £2500
opennlp OpenNLP for Text Based Machine Learning Portsmouth Technopole 14 hours Wed, 2018-03-28 09:30 £2200 / £2500
matlabml1 Introduction to Machine Learning with MATLAB Portsmouth 21 hours Wed, 2018-03-28 09:30 £3300 / £3750
mlintro Introduction to Machine Learning Portsmouth Technopole 7 hours Thu, 2018-03-29 09:30 £1300 / £1450
cpde Data Engineering on Google Cloud Platform Portsmouth 32 hours Mon, 2018-04-02 09:30 £5500 / £6100
bspkannmldt Artificial Neural Networks, Machine Learning and Deep Thinking Portsmouth Technopole 21 hours Tue, 2018-04-03 09:30 £3300 / £3750
Fairsec Fairsec: Setting up a CNN-based machine translation system Portsmouth Technopole 7 hours Tue, 2018-04-03 09:30 £1100 / £1250
predio Machine Learning with PredictionIO Portsmouth Technopole 21 hours Wed, 2018-04-04 09:30 £3300 / £3750
facebooknmt Facebook NMT: Setting up a Neural Machine Translation System Portsmouth Technopole 7 hours Wed, 2018-04-04 09:30 £1100 / £1250
Torch Torch: Getting started with Machine and Deep Learning Portsmouth Technopole 21 hours Wed, 2018-04-04 09:30 £3900 / £4350
mlfunpython Machine Learning Fundamentals with Python Portsmouth Technopole 14 hours Wed, 2018-04-04 09:30 £2200 / £2500
aiauto Artificial Intelligence in Automotive Portsmouth Technopole 14 hours Thu, 2018-04-05 09:30 £2600 / £2900
bspkannmldt Artificial Neural Networks, Machine Learning and Deep Thinking Portsmouth 21 hours Mon, 2018-04-09 09:30 £3300 / £3750
dmmlr Data Mining & Machine Learning with R Portsmouth 14 hours Tue, 2018-04-10 09:30 £2600 / £2900
OpenNN OpenNN: Implementing neural networks Portsmouth 14 hours Tue, 2018-04-10 09:30 £2600 / £2900

Course Outlines

Code Name Duration Outline
predio Machine Learning with PredictionIO 21 hours

PredictionIO is an open source Machine Learning Server built on top of state-of-the-art open source stack.

Audience

This course is directed at developers and data scientists who want to create predictive engines for any machine learning task.

Fairsec Fairsec: Setting up a CNN-based machine translation system 7 hours

Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT).

In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Source and target language content samples can be prepared according to audience's requirements.

Audience

  • Localization specialists with a technical background
  • Global content managers
  • Localization engineers
  • Software developers in charge of implementing global content solutions

Format of the course
    Part lecture, part discussion, heavy hands-on practice

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).

Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.

Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

  • have a good understanding on deep neural networks(DNN), CNN and RNN

  • understand TensorFlow’s structure and deployment mechanisms

  • be able to carry out installation / production environment / architecture tasks and configuration

  • be able to assess code quality, perform debugging, monitoring

  • be able to implement advanced production like training models, building graphs and logging
     

Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject.

The Duration of the complete course will be around 70 hours and not 35 hours.

systemml Apache SystemML for Machine Learning 14 hours

Apache SystemML is a distributed and declarative machine learning platform.

SystemML provides declarative large-scale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations, to distributed computations on Apache Hadoop and Apache Spark.

Audience

This course is suitable for Machine Learning researchers, developers and engineers seeking to utilize SystemML as a framework for machine learning.

cpde Data Engineering on Google Cloud Platform 32 hours

This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.

This course teaches participants the following skills:

  • Design and build data processing systems on Google Cloud Platform
  • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
  • Derive business insights from extremely large datasets using Google BigQuery
  • Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML
  • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
  • Enable instant insights from streaming data

This class is intended for experienced developers who are responsible for managing big data transformations including:

  • Extracting, Loading, Transforming, cleaning, and validating data
  • Designing pipelines and architectures for data processing
  • Creating and maintaining machine learning and statistical models
  • Querying datasets, visualizing query results and creating reports
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 command-line 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 instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text.

By the end of this training, participants will be able to:

  • Use a command-line 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 hands-on practice
aiintrozero From Zero to AI 35 hours

This course is created for people who have no previous experience in probability and statistics.

Fairseq Fairseq: Setting up a CNN-based machine translation system 7 hours

Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT).

In this training participants will learn how to use Fairseq to carry out translation of sample content.

By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution.

Audience

  • Localization specialists with a technical background
  • Global content managers
  • Localization engineers
  • Software developers in charge of implementing global content solutions

Format of the course
    Part lecture, part discussion, heavy hands-on practice

Note

  • If you wish to use specific source and target language content, please contact us to arrange.
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 instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world 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 hands-on practice
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.

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.

facebooknmt Facebook NMT: Setting up a Neural Machine Translation System 7 hours

Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT).

In this training participants will learn how to use Fairseq to carry out translation of sample content.

By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution.

Audience

  • Localization specialists with a technical background
  • Global content managers
  • Localization engineers
  • Software developers in charge of implementing global content solutions

Format of the course

  • Part lecture, part discussion, heavy hands-on practice

Note

  • If you wish to use specific source and target language content, please contact us to arrange.
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.

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.

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 instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.

By the end of this training, participants will be able to:

  • Programmatically create training sets to enable the labeling of massive training sets
  • Train high-quality end models by first modeling noisy training sets
  • Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
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.

mldt Machine Learning and Deep Learning 21 hours

This course covers AI (emphasizing Machine Learning and Deep Learning)

dsstne Amazon DSSTNE: Build a recommendation system 7 hours

Amazon DSSTNE is an open-source library for training and deploying recommendation models. It allows models with weight matrices that are too large for a single GPU to be trained on a single host.

In this instructor-led, live training, participants will learn how to use DSSTNE to build a recommendation application.

By the end of this training, participants will be able to:

  • Train a recommendation model with sparse datasets as input
  • Scale training and prediction models over multiple GPUs
  • Spread out computation and storage in a model-parallel fashion
  • Generate Amazon-like personalized product recommendations
  • Deploy a production-ready application that can scale at heavy workloads

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
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.

deepmclrg Machine Learning & Deep Learning with Python and R 14 hours
pythonadvml Python for Advanced Machine Learning 21 hours

In this instructor-led, live training, participants will learn the most relevant and cutting-edge 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 semi-supervised 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 hands-on practice
annmldt Artificial Neural Networks, Machine Learning, Deep Thinking 21 hours
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 instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.

By the end of this training, participants will be able to:

  • Use techniques as hyper-parameter tuning and deep learning
  • Understand and implement unsupervised learning techniques
  • Put a model into production for use in a larger application

Audience

  • Developers
  • Analysts
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
bspkaml Machine Learning 21 hours
This course will be a combination of theory and practical work with specific examples used throughout the event.
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 hands-on exercises, instructor feedback, and testing of knowledge and skills acquired.

Audience
    Data analysts
    PhD students, researchers and practitioners

 

encogadv Encog: Advanced Machine Learning 14 hours

Encog is an open-source machine learning framework for Java and .Net.

In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models.

By the end of this training, participants will be able to:

  • Implement different neural networks optimization techniques to resolve underfitting and overfitting
  • Understand and choose from a number of neural network architectures
  • Implement supervised feed forward and feedback networks

Audience

  • Developers
  • Analysts
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
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
    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.

encogintro Encog: Introduction to Machine Learning 14 hours

Encog is an open-source machine learning framework for Java and .Net.

In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored.

By the end of this training, participants will be able to:

  • Prepare data for neural networks using the normalization process
  • Implement feed forward networks and propagation training methodologies
  • Implement classification and regression tasks
  • Model and train neural networks using Encog's GUI based workbench
  • Integrate neural network support into real-world applications

Audience

  • Developers
  • Analysts
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
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 real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned.

By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects.

Audience
    Software developers and programmers wishing to enable Machine and Deep Learning within their applications

Format of the course
    Overview of Machine and Deep Learning
    In-class coding and integration exercises
    Test questions sprinkled along the way to check understanding

pythontextml Python: Machine Learning with Text 21 hours

In this instructor-led, live training, participants will learn how to use the right machine learning and NLP (Natural Language Processing) techniques to extract value from text-based data.

By the end of this training, participants will be able to:

  • Solve text-based data science problems with high-quality, reusable code
  • Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems
  • Build effective machine learning models using text-based 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 hands-on practice
bspkannmldt Artificial Neural Networks, Machine Learning and Deep Thinking 21 hours
OpenNN OpenNN: Implementing neural networks 14 hours

OpenNN is an open-source class library written in C++  which implements neural networks, for use in machine learning.

In this course we go over the principles of neural networks and use OpenNN to implement a sample application.

Audience
    Software developers and programmers wishing to create Deep Learning applications.

Format of the course
    Lecture and discussion coupled with hands-on exercises.

mlios Machine Learning on iOS 14 hours

In this instructor-led, live training, participants will learn how to use the iOS Machine Learning (ML) technology stack as they as they step through the creation and deployment of an iOS mobile app.

By the end of this training, participants will be able to:

  • Create a mobile app capable of image processing, text analysis and speech recognition
  • Access pre-trained ML models for integration into iOS apps
  • Create a custom ML model
  • Add Siri Voice support to iOS apps
  • Understand and use frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit
  • Use languages and tools such as Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder

Audience

  • Developers

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
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, real-world understanding of Big Data and its related technologies, methodologies and tools.

Participants will have the opportunity to put this knowledge into practice through hands-on 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, hands-on practice and implementation, occasional quizing to measure progress.
matlabdl Matlab for Deep Learning 14 hours

In this instructor-led, 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 TensorFlow-Keras
  • 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 hands-on practice
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.

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 three-day training provides comprehensive information on moving around the environment and performing the OCTAVE package for data analysis and engineering calculations. The training recipients are beginners but also those who know the program and would like to systematize their knowledge and improve their skills. Knowledge of other programming languages is not required, but it will greatly facilitate the learners' acquisition of knowledge. The course will show you how to use the program in many practical examples.

mlbankingr Machine Learning for Banking (with R) 28 hours

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world 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 hands-on practice
dmmlr Data Mining & Machine Learning with R 14 hours
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

  1. Investors and AI entrepreneurs
  2. Managers and Engineers whose company is venturing into AI space
  3. Business Analysts & Investors
mlbankingpython_ Machine Learning for Banking (with Python) 21 hours

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world 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 hands-on practice
cpb100 Google Cloud Platform Fundamentals: Big Data & Machine Learning 8 hours

This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform.

This course teaches participants the following skills:

  • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.
  • Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.
  • Employ BigQuery and Cloud Datalab to carry out interactive data analysis.
  • Train and use a neural network using TensorFlow.
  • Employ ML APIs.
  • Choose between different data processing products on the Google Cloud Platform.

This class is intended for the following:

  • Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform.
  • Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports.
  • Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists.
opennmt OpenNMT: Setting up a Neural Machine Translation System 7 hours

OpenNMT is a full-featured, open-source (MIT) neural machine translation system that utilizes the Torch mathematical toolkit.

In this training participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor. By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.

Source and target language samples will be pre-arranged per the audience's requirements.

Audience

  • Localization specialists with a technical background
  • Global content managers
  • Localization engineers
  • Software developers in charge of implementing global content solutions

Format of the course

  • Part lecture, part discussion, heavy hands-on practice
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, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution.

In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises.

By the end of this training, participants will be able to:

  • Install and configure OpenNLP
  • Download existing models as well as create their own
  • Train the models on various sets of sample data
  • Integrate OpenNLP with existing Java applications

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
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