Local, instructor-led live Artificial Intelligence (AI) training courses demonstrate through hands-on practice how to implement AI solutions for solving real-world problems.

AI training is available as "onsite live training" or "remote live training". Canada onsite live Artificial Intelligence (AI) trainings can be carried out locally on customer premises or in NobleProg corporate training centers. Remote live training is carried out by way of an interactive, remote desktop.

NobleProg -- Your Local Training Provider

Testimonials

★★★★★

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

Mohamed Salama

Course: Data Mining & Machine Learning with R

I liked that this course had very interesting subject.

Wojciech Wilk

Course: Introduction to Deep Learning

I really appreciated the crystal clear answers of Chris to our questions.

Léo Dubus

Course: Neural Networks Fundamentals using TensorFlow as Example

I generally enjoyed the knowledgeable trainer.

Sridhar Voorakkara

Course: 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

Course: Neural Networks Fundamentals using TensorFlow as Example

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

Kieran Conboy

Course: 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

Course: Neural Networks Fundamentals using TensorFlow as Example

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

Course: 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

Course: Machine Learning and Deep Learning

I really enjoyed the coverage and depth of topics.

Anirban Basu

Course: Machine Learning and Deep Learning

I liked the new insights in deep machine learning.

Josip Arneric

Course: Neural Network in R

We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.

Tea Poklepovic

Course: Neural Network in R

I mostly enjoyed the graphs in R :))).

Faculty of Economics and Business Zagreb

Course: Neural Network in R

The deep knowledge of the trainer about the topic.

Sebastian Görg

Course: Introduction to Deep Learning

Very updated approach or CPI (tensor flow, era, learn) to do machine learning.

Paul Lee

Course: TensorFlow for Image Recognition

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

Course: Neural Networks Fundamentals using TensorFlow as Example

I was benefit from topic selection. Style of training. Practice orientation.

Commerzbank AG

Course: Neural Networks Fundamentals using TensorFlow as Example

In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.

Sacha Nandlall

Course: Python for Advanced Machine Learning

This is one of the best hands-on with exercises programming courses I have ever taken.

Laura Kahn

Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP

This is one of the best quality online training I have ever taken in my 13 year career. Keep up the great work!.

Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP

Richard's training style kept it interesting, the real world examples used helped to drive the concepts home.

Jamie Martin-Royle - NBrown Group

Course: From Data to Decision with Big Data and Predictive Analytics

The content, as I found it very interesting and think it would help me in my final year at University.

Krishan Mistry - NBrown Group

Course: From Data to Decision with Big Data and Predictive Analytics

I genuinely liked excercises

L M ERICSSON LIMITED

Course: Machine Learning

I liked the lab exercises.

Marcell Lorant - L M ERICSSON LIMITED

Course: Machine Learning

The Jupyter notebook form, in which the training material is available

L M ERICSSON LIMITED

Course: Machine Learning

There were many exercises and interesting topics.

L M ERICSSON LIMITED

Course: Machine Learning

Some great lab exercises analyzed and explained by the trainer in depth (e.g. covariants in linear regression, matching the real function)

L M ERICSSON LIMITED

Course: Machine Learning

It's just great that all material including the exercises is on the same page and then it gets updated on the fly. The solution is revealed at the end. Cool! Also, I do appreciate that Krzysztof took extra effort to understand our problems and suggested us possible techniques.

Attila Nagy - L M ERICSSON LIMITED

Course: Machine Learning

The easy use of the VideoCapture functionality to acquire video images from laptop camera.

HP Printing and Computing Solutions, Sociedad Limitada Unipe

Course: Computer Vision with OpenCV

I enjoyed the advises given by the trainer about how to use the tools. This is something that can't be got from the internet and are very useful.

HP Printing and Computing Solutions, Sociedad Limitada Unipe

Course: Computer Vision with OpenCV

I enjoyed the advises given by the trainer about how to use the tools. This is something that can't be got from the internet and are very useful.

HP Printing and Computing Solutions, Sociedad Limitada Unipe

Course: Computer Vision with OpenCV

It was easy to follow.

HP Printing and Computing Solutions, Sociedad Limitada Unipe

Course: Computer Vision with OpenCV

the matter was well presented and in an orderly manner.

Marylin Houle - Ivanhoe Cambridge

Course: Introduction to R with Time Series Analysis

I was benefit from the passion to teach and focusing on making thing sensible.

Zaher Sharifi - GOSI

Course: Advanced Deep Learning

It is one on one. I can ask a lot of question and also ask the trainner to repeat when I was not clear about some stuff.

Course: Insurtech: A Practical Introduction for Managers

The informal exchanges we had during the lectures really helped me deepen my understanding of the subject

Explore

Course: Deep Reinforcement Learning with Python

The trainer's patience

European Space Agency (ESA/ESTEC)

Course: Getting Started with Quantum Computing and Q#

It is showing many methods with pre prepared scripts- very nicely prepared materials & easy to traceback

Kamila Begej - GE Medical Systems Polska Sp. Zoo

Course: Machine Learning – Data science

I like that training was focused on examples and coding. I thought that it is impossible to pack so much content into three days of training, but I was wrong. Training covered many topics and everything was done in a very detailed manner (especially tuning of model's parameters - I didn't expected that there will be a time for this and I was gratly surprised).

Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo

Course: Machine Learning – Data science

the last day. generation part

Accenture Inc

Course: Python for Natural Language Generation

The topics referring to NLG. The team was able to learn something new in the end with topics that were interesting but it was only in the last day. There were also more hands on activities than slides which was good.

Accenture Inc

Course: Python for Natural Language Generation

I like that it focuses more on the how-to of the different text summarization methods

Course: Text Summarization with Python

The remote classroom setting worked very well

Trimac Management Services LP

Course: Introduction to R with Time Series Analysis

Good detail on what R is used for and how to start using it right away

Hoss Shenassa - Trimac Management Services LP

Course: Introduction to R with Time Series Analysis

The many practical examples / assignments that we went through were great. For me, I learn better by seeing examples and applying them elsewhere. The use of real data and applying what was taught against it was extremely valuable. Michaels PowerPoint presentations and his ability to work through each solution was invaluable.

Trimac Management Services LP

Course: Introduction to R with Time Series Analysis

lots of information, all questions ansered, interesting examples

A1 Telekom Austria AG

Course: Deep Learning for Telecom (with Python)

The exercises.

Elena Velkova - CEED Bulgaria

Course: Predictive Modelling with R

Practical exercises with R were very helpful.

CEED Bulgaria

Course: Predictive Modelling with R

About face area.

中移物联网

Course: Deep Learning for NLP (Natural Language Processing)

This is one of the best quality online training I have ever taken in my 13 year career. Keep up the great work!.

Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP

It is one on one. I can ask a lot of question and also ask the trainner to repeat when I was not clear about some stuff.

Course: Insurtech: A Practical Introduction for Managers

I like that it focuses more on the how-to of the different text summarization methods

Course: Text Summarization with Python

Some of our clients

AI (Artificial Intelligence) Subcategories in Canada

AI (Artificial Intelligence) Course Outlines in Canada

Course Name

Duration

Overview

Course Name

Duration

Overview

Artificial Intelligence in Automotive

14 hours

Overview

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.

Natural Language Processing

21 hours

Overview

This course has been designed for people interested in extracting meaning from written English text, though the knowledge can be applied to other human languages as well.

The course will cover how to make use of text written by humans, such as blog posts, tweets, etc...

For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source.

Machine Learning with PredictionIO

21 hours

Overview

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.

Pattern Matching

14 hours

Overview

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.

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.

PaddlePaddle

21 hours

Overview

PaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu.

In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications.

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

- Set up and configure PaddlePaddle
- Set up a Convolutional Neural Network (CNN) for image recognition and object detection
- Set up a Recurrent Neural Network (RNN) for sentiment analysis
- Set up deep learning on recommendation systems to help users find answers
- Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.

Audience

- Developers
- Data scientists

Format of the course

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

OptaPlanner in Practice

21 hours

Overview

This course uses a practical approach to teaching OptaPlanner. It provides participants with the tools needed to perform the basic functions of this tool.

OpenNN: Implementing Neural Networks

14 hours

Overview

In this instructor-led, live training, we go over the principles of neural networks and use OpenNN to implement a sample application.

Format of the course

- Lecture and discussion coupled with hands-on exercises.

OpenNMT: Setting Up a Neural Machine Translation System

7 hours

Overview

In this instructor-led, live 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.

Format of the Course

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

OpenNLP for Text Based Machine Learning

14 hours

Overview

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

OpenFace: Creating Facial Recognition Systems

14 hours

Overview

OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research.

In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application.

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

- Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
- Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.

Audience

- Developers
- Data scientists

Format of the course

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

Computer Vision with OpenCV

28 hours

Overview

OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms.

Audience

This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects

Octave not only for programmers

21 hours

Overview

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.

Neural computing – Data science

14 hours

Overview

This classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries

Natural Language Processing - AI/Robotics

21 hours

Overview

This classroom based training session will explore NLP techniques in conjunction with the application of AI and Robotics in business. Delegates will undertake computer based examples and case study solving exercises using Python

NLP: Natural Language Processing with R

21 hours

Overview

It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.

This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.

By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.

Format of the Course

- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

Python for Natural Language Generation

21 hours

Overview

Natural language generation (NLG) refers to the production of natural language text or speech by a computer.

In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content.

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

- Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
- Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
- Understand the NLG pipeline and apply the right techniques at each stage
- Understand the architecture of a Natural Language Generation (NLG) system
- Implement the most suitable algorithms and models for analysis and ordering
- Pull data from publicly available data sources as well as curated databases to use as material for generated text
- Replace manual and laborious writing processes with computer-generated, automated content creation

Audience

- Developers
- Data scientists

Format of the course

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

Python for Advanced Machine Learning

21 hours

Overview

In this instructor-led, live training in Canada, 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.

Neural Networks Fundamentals using TensorFlow as Example

28 hours

Overview

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 to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.

Introduction to the use of neural networks

7 hours

Overview

The training is aimed at people who want to learn the basics of neural networks and their applications.

Machine Learning – Data science

21 hours

Overview

This classroom based training session will explore machine learning tools with (suggested) Python. Delegates will have computer based examples and case study exercises to undertake.

Machine Learning for Robotics

21 hours

Overview

This 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.

Machine Learning with Python

21 hours

Overview

The aim of this course is to provide general 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.

Machine Learning in business – AI/Robotics

14 hours

Overview

This classroom based training session will explore machine learning techniques, with computer based examples and case study solving exercises using a relevant programme languauge

Machine Learning on iOS

14 hours

Overview

In this instructor-led, live training, participants will learn how to use the iOS Machine Learning (ML) technology stack 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

Introduction to Machine Learning

7 hours

Overview

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.

Machine Learning Fundamentals with R

14 hours

Overview

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.

Machine Learning Fundamentals with Python

14 hours

Overview

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.

Machine Learning Fundamentals with Scala and Apache Spark

14 hours

Overview

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.

Machine Learning for Finance (with R)

28 hours

Overview

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

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. 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 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 R

Audience

- Developers
- Data scientists

Format of the course

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

Machine Learning for Finance (with Python)

21 hours

Overview

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.

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.

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

Other countries

These courses are also available in other countries ››

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Artificial Intelligence (AI) Training Courses in Canada

Testimonials

Some of our clients

AI (Artificial Intelligence) Subcategories in Canada

AI (Artificial Intelligence) Course Outlines in Canada

Other regions in Canada

Other countries

Consulting in Canada

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