Machine Learning Training in London

Machine Learning Training in London

Local, instructor-led live Machine Learning training courses demonstrate through hands-on practice how to apply machine learning techniques and tools for solving real-world problems in various industries. NobleProg ML courses cover different programming languages and frameworks, including Python, R language and Matlab. Machine Learning courses are offered for a number of industry applications, including Finance, Banking and Insurance and cover the fundamentals of Machine Learning as well as more advanced approaches such as Deep Learning. Machine Learning training is available as "onsite live training" or "remote live training". Onsite live training can be carried out locally on customer premises in London or in NobleProg corporate training centers in London. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider

London, Hatton Garden
Learn Machine Learning in our training center in London.

Overview

Located near Farringdon and Chancery Lane.

A stylish and impressive eight-storey Art Deco building ideal for hosting a range of training, conferences and meetings near Farringdon and Chancery Lane stations.

Directions

The Hatton is located in London's Diamond District in Hatton Garden.

By Underground, Farringdon Station (Metropolitan, Circle, Hammersmith and City lines)
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Machine Learning Course Events - London

CodeNameVenueDurationCourse DateCourse Price [Remote / Classroom]
caffeDeep Learning for Vision with CaffeLondon, Hatton Garden21 hoursMon, 2018-10-08 09:30£3900 / £5025
matlabdlMatlab for Deep LearningLondon, Hatton Garden14 hoursMon, 2018-10-08 09:30£2600 / £3350
deeplearning1Introduction to Deep LearningLondon, Hatton Garden21 hoursTue, 2018-10-09 09:30£3900 / £5025
predioMachine Learning with PredictionIOLondon, Hatton Garden21 hoursTue, 2018-10-09 09:30£3900 / £5025
annmldtArtificial Neural Networks, Machine Learning, Deep ThinkingLondon, Hatton Garden21 hoursTue, 2018-10-09 09:30£3900 / £5025
dlvDeep Learning for VisionLondon, Hatton Garden21 hoursTue, 2018-10-09 09:30£3900 / £5025
mllbgMachine Learning in business – AI/RoboticsLondon, Hatton Garden14 hoursTue, 2018-10-09 09:30£2600 / £3350
t2tT2T: Creating Sequence to Sequence Models for Generalized LearningLondon, Hatton Garden7 hoursTue, 2018-10-09 09:30£1100 / £1475
encogadvEncog: Advanced Machine LearningLondon, Hatton Garden14 hoursTue, 2018-10-09 09:30£2600 / £3350
embeddingprojectorEmbedding Projector: Visualizing Your Training DataLondon, Hatton Garden14 hoursWed, 2018-10-10 09:30£2200 / £2950
tensorflowservingTensorFlow ServingLondon, Hatton Garden7 hoursWed, 2018-10-10 09:30£1300 / £1675
mlbankingpython_Machine Learning for Banking (with Python)London, Hatton Garden21 hoursWed, 2018-10-10 09:30£3900 / £5025
aifortelecomAI Awareness for TelecomLondon, Hatton Garden14 hoursWed, 2018-10-10 09:30£2600 / £3350
mlintroIntroduction to Machine LearningLondon, Hatton Garden7 hoursMon, 2018-10-15 09:30£1300 / £1675
bspkannmldtArtificial Neural Networks, Machine Learning and Deep ThinkingLondon, Hatton Garden21 hoursMon, 2018-10-15 09:30£3900 / £5025
aiintrozeroFrom Zero to AILondon, Hatton Garden35 hoursMon, 2018-10-15 09:30£6500 / £8375
tfirTensorFlow for Image RecognitionLondon, Hatton Garden28 hoursMon, 2018-10-15 09:30£5200 / £6700
w2vdl4jNLP with Deeplearning4jLondon, Hatton Garden14 hoursMon, 2018-10-15 09:30£2600 / £3350
NeuralnettfNeural Networks Fundamentals using TensorFlow as ExampleLondon, Hatton Garden28 hoursMon, 2018-10-15 09:30£5200 / £6700
opennlpOpenNLP for Text Based Machine LearningLondon, Hatton Garden14 hoursMon, 2018-10-15 09:30£2600 / £3350
dlfornlpDeep Learning for NLP (Natural Language Processing)London, Hatton Garden28 hoursMon, 2018-10-15 09:30£5200 / £6700
dlfinancewithrDeep Learning for Finance (with R)London, Hatton Garden28 hoursMon, 2018-10-15 09:30£5200 / £6700
dlforbankingwithrDeep Learning for Banking (with R)London, Hatton Garden28 hoursMon, 2018-10-15 09:30£5200 / £6700
dlforfinancewithpythonDeep Learning for Finance (with Python)London, Hatton Garden28 hoursMon, 2018-10-15 09:30£5200 / £6700
intelligentmobileappsBuilding Intelligent Mobile ApplicationsLondon, Hatton Garden35 hoursMon, 2018-10-15 09:30£5500 / £7375
MLFWR1Machine Learning Fundamentals with RLondon, Hatton Garden14 hoursTue, 2018-10-16 09:30£2600 / £3350
deeplrn深度学习基础与实战London, Hatton Garden14 hoursTue, 2018-10-16 09:30£2200 / £2950
mlfinancepythonMachine Learning for Finance (with Python)London, Hatton Garden21 hoursTue, 2018-10-16 09:30£3900 / £5025
mlfsasMachine Learning Fundamentals with Scala and Apache SparkLondon, Hatton Garden14 hoursWed, 2018-10-17 09:30£2600 / £3350
openfaceOpenFace: Creating Facial Recognition SystemsLondon, Hatton Garden14 hoursWed, 2018-10-17 09:30£2600 / £3350

Machine Learning Course Outlines in London

CodeNameDurationOverview
aiintArtificial Intelligence Overview7 hoursThis course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
mldtMachine Learning and Deep Learning21 hoursThis course covers AI (emphasizing Machine Learning and Deep Learning)
dsstneAmazon DSSTNE: Build a Recommendation System7 hoursAmazon 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
mldlnlpintroML、DL與NLP入門與進階大綱14 hoursThe 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.
snorkelSnorkel: Rapidly Process Training Data7 hoursSnorkel 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
facebooknmtFacebook NMT: Setting up a Neural Machine Translation System7 hoursFacebook NMT (Fairseq) is an open-source sequence-to-sequence learning toolkit created by Facebook 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.
intrdplrngrsneuingIntroduction Deep Learning & Neural Networks for Engineers21 hoursArtificial intelligence has revolutionized a large number of economic sectors (industry, medicine, communication, etc.) after having upset many scientific fields. Nevertheless, his presentation in the major media is often a fantasy, far removed from what really are the fields of Machine Learning or Deep Learning. The aim of this course is to provide engineers who already have a master's degree in computer tools (including a software programming base) an introduction to Deep Learning as well as to its various fields of specialization and therefore to the main existing network architectures today. If the mathematical bases are recalled during the course, a level of mathematics of type BAC + 2 is recommended for more comfort. It is absolutely possible to ignore the mathematical axis in order to maintain only a "system" vision, but this approach will greatly limit your understanding of the subject.
opennmtOpenNMT: Setting Up a Neural Machine Translation System7 hoursOpenNMT 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
mlentreMachine Learning Concepts for Entrepreneurs and Managers21 hoursThis training course is for people that would like to apply Machine Learning in practical applications for their team. The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same.

Target Audience

- Investors and AI entrepreneurs
- Managers and Engineers whose company is venturing into AI space
- Business Analysts & Investors
octnpOctave not only for programmers21 hoursCourse 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.
OpenNNOpenNN: Implementing Neural Networks14 hoursOpenNN 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.
TorchTorch: Getting started with Machine and Deep Learning21 hoursTorch 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
datamodelingPattern Recognition35 hoursThis 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
wolfdataData Science: Analysis and Presentation7 hoursThe 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.
aiautoArtificial Intelligence in Automotive14 hoursThis 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.
mlintroIntroduction to Machine Learning7 hoursThis 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.
aiintrozeroFrom Zero to AI35 hoursThis course is created for people who have no previous experience in probability and statistics.
systemmlApache SystemML for Machine Learning14 hoursApache 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.
predioMachine Learning with PredictionIO21 hoursPredictionIO 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.
dmmlrData Mining & Machine Learning with R14 hoursR is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.
mlfsasMachine Learning Fundamentals with Scala and Apache Spark14 hoursThe 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.
bspkannmldtArtificial Neural Networks, Machine Learning and Deep Thinking21 hoursArtificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
matlabml1Introduction to Machine Learning with MATLAB21 hoursMATLAB is a numerical computing environment and programming language developed by MathWorks.
mlrobot1Machine Learning for Robotics21 hoursThis course introduces 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 a short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software.
bspkamlMachine Learning21 hoursThis course will be a combination of theory and practical work with specific examples used throughout the event.
annmldtArtificial Neural Networks, Machine Learning, Deep Thinking21 hoursArtificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
MLFWR1Machine Learning Fundamentals with R14 hoursThe 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.
mlfunpythonMachine Learning Fundamentals with Python14 hoursThe 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.
appliedmlApplied Machine Learning14 hoursThis 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.
pythonadvmlPython for Advanced Machine Learning21 hoursIn 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
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Course Discounts

Course Venue Course Date Course Price [Remote / Classroom]
Selenium WebDriver in C#: Introduction to Web Testing Automation in C# Sheffield Wed, 2018-09-26 09:30 £2178 / £2578
Introduction to Ansible Automation London, Hatton Garden Mon, 2018-10-08 09:30 £1089 / £1464
Jenkins: Continuous Integration for Agile Development Manchester, King Street Thu, 2018-10-18 09:30 £2574 / £3224
Introduction to Recommendation Systems Swansea- Princess House Thu, 2018-10-18 09:30 £990 / £1140
Impact Evaluation – Quantitative Analysis London, Hatton Garden Wed, 2018-10-24 09:30 £2574 / £3324
CakePHP: Rapid Web Application Development Birmingham Tue, 2018-11-06 09:30 £4356 / £5656

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