Online or onsite, instructor-led live Machine Learning (ML) 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 "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. The US onsite live Machine Learning trainings can be carried out locally on customer premises or in NobleProg corporate training centers.

NobleProg -- Your Local Training Provider

Testimonials

★★★★★

Tomasz really know the information well and the course was well paced.

Raju Krishnamurthy - Google

Course: TensorFlow Extended (TFX)

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

Course: Artificial Neural Networks, Machine Learning, Deep Thinking

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

Mohamed Salama

Course: Data Mining & Machine Learning with R

The topic is very interesting.

Wojciech Baranowski

Course: Introduction to Deep Learning

Trainers theoretical knowledge and willingness to solve the problems with the participants after the training.

Grzegorz Mianowski

Course: Introduction to Deep Learning

Topic. Very interesting!.

Piotr

Course: Introduction to Deep Learning

Exercises after each topic were really helpful, despite there were too complicated at the end. In general, the presented material was very interesting and involving! Exercises with image recognition were great.

Dolby Poland Sp. z o.o.

Course: Introduction to Deep Learning

I think that if training would be done in polish it would allow the trainer to share his knowledge more efficient.

Radek

Course: Introduction to Deep Learning

The global overview of deep learning.

Bruno Charbonnier

Course: Advanced Deep Learning

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

Alexandre GIRARD

Course: Advanced Deep Learning

Doing exercises on real examples using Eras. Italy totally understood our expectations about this training.

Paul Kassis

Course: Advanced Deep Learning

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

Léo Dubus

Course: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple

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

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

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

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

Zaher Sharifi - GOSI

Course: Advanced Deep Learning

About face area.

中移物联网

Course: Deep Learning for NLP (Natural Language Processing)

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

Explore

Course: Deep Reinforcement Learning with Python

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 surprized).

Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo

Course: Machine Learning – Data science

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

Course: Text Summarization with Python

lots of information, all questions ansered, interesting examples

A1 Telekom Austria AG

Course: Deep Learning for Telecom (with Python)

The trainer was a professional in the subject field and related theory with application excellently

Fahad Malalla - Tatweer Petroleum

Course: Applied AI from Scratch in Python

I started with close to zero knowledge, and by the end I was able to build and train my own networks.

Huawei Technologies Duesseldorf GmbH

Course: TensorFlow for Image Recognition

Ewa has a passion for the subject and a huge wealth of knowledge. She impressed all of us with her knowledge and kept us all focused through the day.

Rock Solid Knowledge Ltd

Course: Machine Learning – Data science

Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain

Richard Blewett - Rock Solid Knowledge Ltd

Course: Machine Learning – Data science

So much breadth and topics covered. I felt it was a huge subject to try and cover in 3 days - the trainer did what they could to cover everything almost exactly on time!

Rock Solid Knowledge Ltd

Course: Machine Learning – Data science

Adjusting to our needs

Sumitomo Mitsui Finance and Leasing Company, Limited

Course: Kubeflow

Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject

Ali Kattan - TWPI

Course: Natural Language Processing with TensorFlow

the way he present everything with examples and training was so useful

Ibrahim Mohammedameen - TWPI

Course: Natural Language Processing with TensorFlow

Very knowledgeable

Usama Adam - TWPI

Course: Natural Language Processing with TensorFlow

The excersise where we should train a network to approximate a function

Nercia Utbildning AB

Course: Deep Learning with TensorFlow 2.0

having access to the notebooks to work through

Premier Partnership

Course: Python for Advanced Machine Learning

The trainers knowledge of the topics he was teaching.

Premier Partnership

Course: Python for Advanced Machine Learning

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

Course: Text Summarization with Python

Machine Learning Subcategories

ML (Machine Learning) Course Outlines

Course Name

Duration

Overview

Course Name

Duration

Overview

Exploring Generative Pre-trained Transformers (GPT): From GPT-3 to GPT-4

14 hours

This instructor-led, live training in the US (online or onsite) is aimed at data scientists, machine learning engineers, NLP researchers, and AI enthusiasts who wish to understand the inner workings of GPT models, explore the capabilities of GPT-3 and GPT-4, and learn how to leverage these models for their NLP tasks. By the end of this training, participants will be able to:

Understand the key concepts and principles behind Generative Pre-trained Transformers.
Comprehend the architecture and training process of GPT models.
Utilize GPT-3 for tasks such as text generation, completion, and translation.
Explore the latest advancements in GPT-4 and its potential applications.
Apply GPT models to their own NLP projects and tasks.

LightGBM for Machine Learning

21 hours

This instructor-led, live training in the US (online or onsite) is aimed at beginner to intermediate-level developers and data scientists who wish to learn the basics of LightGBM and explore advanced techniques. By the end of this training, participants will be able to:

Install and configure LightGBM.
Understand the theory behind gradient boosting and decision tree algorithms
Use LightGBM for basic and advanced machine learning tasks.
Implement advanced techniques such as feature engineering, hyperparameter tuning, and model interpretation.
Integrate LightGBM with other machine learning frameworks.
Troubleshoot common issues in LightGBM.

Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation

21 hours

This instructor-led, live training in the US (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation. By the end of this training, participants will be able to:

Understand advanced deep learning architectures and techniques for text-to-image generation.
Implement complex models and optimizations for high-quality image synthesis.
Optimize performance and scalability for large datasets and complex models.
Tune hyperparameters for better model performance and generalization.
Integrate Stable Diffusion with other deep learning frameworks and tools.

Vertex AI

7 hours

This instructor-led, live training in the US (online or onsite) is aimed at beginner to intermediate-level software engineers or anyone who wish to learn how to use Vertex AI to perform and complete machine learning activities. By the end of this training, participants will be able to:

Understand how Vertex AI works and use it as a machine learning platform.
Learn about machine learning and NLP concepts.
Know how to train and deploy machine learning models using Vertex AI.

DeepSpeed for Deep Learning

21 hours

This instructor-led, live training in the US (online or onsite) is aimed at beginner to intermediate-level data scientists and machine learning engineers who wish to improve the performance of their deep learning models. By the end of this training, participants will be able to:

Understand the principles of distributed deep learning.
Install and configure DeepSpeed.
Scale deep learning models on distributed hardware using DeepSpeed.
Implement and experiment with DeepSpeed features for optimization and memory efficiency.

AlphaFold

7 hours

This instructor-led, live training in the US (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies. By the end of this training, participants will be able to:

Understand the basic principles of AlphaFold.
Learn how AlphaFold works.
Learn how to interpret AlphaFold predictions and results.

Introduction to Stable Diffusion for Text-to-Image Generation

21 hours

This instructor-led, live training in the US (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases. By the end of this training, participants will be able to:

Understand the principles of Stable Diffusion and how it works for image generation.
Build and train Stable Diffusion models for image generation tasks.
Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
Optimize the performance and stability of Stable Diffusion models.

Data Mining with Weka

14 hours

This instructor-led, live training in the US (online or onsite) is aimed at beginner to intermediate-level data analysts and data scientists who wish to use Weka to perform data mining tasks. By the end of this training, participants will be able to:

Install and configure Weka.
Understand the Weka environment and workbench.
Perform data mining tasks using Weka.

Machine Learning with Python – 2 Days

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.

Advanced Machine Learning with Python

21 hours

In this instructor-led, live training in the US, 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.

Machine Learning with Python – 4 Days

28 hours

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 .

Applied AI from Scratch in Python

28 hours

This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course.

Deep Reinforcement Learning with Python

21 hours

This instructor-led, live training in the US (online or onsite) is aimed at developers and data scientists who wish to learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent. By the end of this training, participants will be able to:

Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning.
Apply advanced Reinforcement Learning algorithms to solve real-world problems.
Build a Deep Learning Agent.

Deep Learning for Telecom (with Python)

28 hours

In this instructor-led, live training in the US, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to:

Understand the fundamental concepts of deep learning.
Learn the applications and uses of deep learning in telecom.
Use Python, Keras, and TensorFlow to create deep learning models for telecom.
Build their own deep learning customer churn prediction model using Python.

Embedding Projector: Visualizing Your Training Data

14 hours

Embedding Projector is an opensource web application for visualizing the data used to train machine learning systems Created by Google, it is part of TensorFlow This instructorled, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project By the end of this training, participants will be able to: Explore how data is being interpreted by machine learning models Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals Explore the properties of a specific embedding to understand the behavior of a model Apply Embedding Project to realworld use cases such building a song recommendation system for music lovers Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy handson practice .

Artificial Intelligence (AI) Overview

7 hours

This 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.

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.

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.

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.

Artificial Neural Networks, Machine Learning, Deep Thinking

21 hours

Artificial 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.

Machine Learning

21 hours

This course will be a combination of theory and practical work with specific examples used throughout the event.

Machine Learning for Robotics

21 hours

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 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.

Data Mining & Machine Learning with R

14 hours

R 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.

From Zero to AI

35 hours

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

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.

Machine Learning Concepts for Entrepreneurs and Managers

21 hours

This training course is for people that would like to apply Machine Learning in practical applications for their team. The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same. Target Audience

Investors and AI entrepreneurs
Managers and Engineers whose company is venturing into AI space
Business Analysts & Investors

Snorkel: Rapidly Process Training Data

7 hours

Snorkel is a system for rapidly creating, modeling, and managing training data It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain In this instructorled, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel By the end of this training, participants will be able to: Programmatically create training sets to enable the labeling of massive training sets Train highquality end models by first modeling noisy training sets Use Snorkel to implement weak supervision techniques and apply data programming to weaklysupervised machine learning systems Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy handson practice .

Encog: Advanced Machine Learning

14 hours

Encog is an opensource machine learning framework for Java andNet In this instructorled, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models By the end of this training, participants will be able to: Implement different neural networks optimization techniques to resolve underfitting and overfitting Understand and choose from a number of neural network architectures Implement supervised feed forward and feedback networks Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy handson practice .

Encog: Introduction to Machine Learning

14 hours

Encog is an opensource machine learning framework for Java andNet In this instructorled, live training, participants will learn how to create various neural network components using ENCOG Realworld case studies will be discussed and machine language based solutions to these problems will be explored By the end of this training, participants will be able to: Prepare data for neural networks using the normalization process Implement feed forward networks and propagation training methodologies Implement classification and regression tasks Model and train neural networks using Encog's GUI based workbench Integrate neural network support into realworld applications Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy handson practice .

Last Updated: 2023-05-15