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 programmeming 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. Onsite live Machine Learning training can be carried out locally on customer premises in the UK or in NobleProg corporate training centres in the UK.
NobleProg -- Your Local Training Provider
Testimonials
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 trainers knowledge of the topics he was teaching.
Premier Partnership
Course: Python for Advanced Machine Learning
Having access to the notebooks to work through
Premier Partnership
Course: Python for Advanced Machine Learning
Trainer was very knowledgeable and open to questions, liked to draw diagrams and explained things in a pretty good way
Course: Deep Learning with TensorFlow 2.0
The details and the presentation style.
Cristian Mititean - Edina Kiss, Accenture Industrial SS
Course: Azure Machine Learning (AML)
Interactive, a lot of exercises
Edina Kiss, Accenture Industrial SS
Course: Azure Machine Learning (AML)
The Exercises
Khaled Altawallbeh - Edina Kiss, Accenture Industrial SS
Course: Azure Machine Learning (AML)
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
Working with real industry-leading ML tools, real datasets and being able to consult with a very experienced data scientist.
Zakład Usługowy Hakoman Andrzej Cybulski
Course: Applied AI from Scratch in Python
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Course: Applied AI from Scratch in 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
There were many exercises and interesting topics.
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
I liked the lab exercises.
Marcell Lorant - L M ERICSSON LIMITED
Course: Machine Learning
The trainer was so knowledgeable and included areas I was interested in
Mohamed Salama
Course: Data Mining & Machine Learning with R
Bardzo merytoryczne szkolenie, bardzo duża wiedza prowadzącego.
Danuta Haber, Orange Szkolenia Sp. z o.o.
Course: Feature Engineering for Machine Learning
Humor prowadzącego.
Danuta Haber, Orange Szkolenia Sp. z o.o.
Course: Feature Engineering for Machine Learning
Wiedza i umiejetnosc jej przekazania
Danuta Haber, Orange Szkolenia Sp. z o.o.
Course: Feature Engineering for Machine Learning
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life™
Course: Kubeflow
Adjusting to our needs
Sumitomo Mitsui Finance and Leasing Company, Limited
Course: Kubeflow
The enthusiasm to the topic. The examples he made an he explained it very well. Sympatic. A little to detailed for beginners. For managers, it could be more abstract in fewer days. But it was designed to fit and we had a good alignment in advance.
Benedikt Chiandetti - HDI Deutschland Bancassurance Kundenservice GmbH
Course: Machine Learning Concepts for Entrepreneurs and Managers
Very very competent trainer who know how to adapt to his audience, and to solve problems Interactive presentation
OLEA MEDICAL
Course: MLflow
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course: MLflow
The knowledge of the trainer was very high and the material was well prepared and organised.
Otilia - Gareth Morgan, TCMT
Course: Machine Learning with Python – 2 Days
I thought the trainer was very knowledgeable and answered questions with confidence to clarify understanding.
Jenna - Gareth Morgan, TCMT
Course: Machine Learning with Python – 2 Days
Convolution filter
Francesco Ferrara - Inpeco SpA
Course: Introduction to Machine Learning
The way of transferring knowledge and the knowledge of the trainer.
Jakub Rękas - Sebastian Pawłowski, Bitcomp Sp. z o.o.
Course: Machine Learning on iOS
The explaination
Wei Yang Teo - Ministry of Defence, Singapore
Course: Machine Learning with Python – 4 Days
The trainer took the time to answer all our questions.
Ministry of Defence, Singapore
Course: Machine Learning with Python – 4 Days
Going through the notebooks, becoming more familiar with Qiskit and the various ways to do things.
Bank of Canada
Course: Practical Quantum Computing
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
The theoretical explanations
Molatelo Tloubatla - University Of South Africa
Course: Data Science: Analysis and Presentation
Machine learning, python, data manipulation
Siphelo Mapolisa - University Of South Africa
Course: Data Science: Analysis and Presentation
Very flexible
Frank Ueltzhöffer
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
Topic. Very interesting!
Piotr
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
The topic is very interesting
Wojciech Baranowski
Course: Introduction to Deep Learning
Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.
Paul Kassis
Course: Advanced Deep Learning
The exercises are sufficiently practical and do not need a high knowledge in Python to be done.
Alexandre GIRARD
Course: Advanced Deep Learning
The global overview of deep learning
Bruno Charbonnier
Course: Advanced Deep Learning
The Colab Notebooks with the training and examples notes.
Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
The exercises were very good and interactive. Instructors were always answering all questions and providing their insight on all topics
Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
lots of information, all questions ansered, interesting examples
A1 Telekom Austria AG
Course: Deep Learning for Telecom (with Python)
Abhi always made sure we were following along. Good mix of practice and theory.
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Course: Deep Reinforcement Learning with Python
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
Explore
Course: Deep Reinforcement Learning with Python
Coverage and depth of topics
Anirban Basu
Course: Machine Learning and Deep Learning
Trainer was very knowledgeable and open to questions, liked to draw diagrams and explained things in a pretty good way
Course: Deep Learning with TensorFlow 2.0
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
ML (Machine Learning) Subcategories
Machine Learning (ML) Course Outlines
Course Name
Duration
Overview
Course Name
Duration
Overview
21 hours
This instructor-led, live training in the UK (online or onsite) is aimed at intermediate-level developers who wish to learn how to use generative AI with LLMs for various tasks and domains.
By the end of this training, participants will be able to:
- Explain what generative AI is and how it works.
- Describe the transformer architecture that powers LLMs.
- Use empirical scaling laws to optimize LLMs for different tasks and constraints.
- Apply state-of-the-art tools and methods to train, fine-tune, and deploy LLMs.
- Discuss the opportunities and risks of generative AI for society and business.
21 hours
This instructor-led, live training in the UK (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.
21 hours
This instructor-led, live training in (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
14 hours
This instructor-led, live training in (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.
7 hours
This instructor-led, live training in the UK (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.
21 hours
This instructor-led, live training in the UK (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.
14 hours
This instructor-led, live training in the UK (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.
By the end of this training, participants will be able to:
- Set up a development environment that includes a popular LLM.
- Create a basic LLM and fine-tune it on a custom dataset.
- Use LLMs for different natural language tasks such as text summarization, question answering, text generation, and more.
- Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
7 hours
This instructor-led, live training in the UK (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.
21 hours
This instructor-led, live training in (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.
14 hours
This instructor-led, live training in the UK (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilize analytical tools for time series forecasting.
By the end of this training, participants will be able to:
- Learn to apply the CRISP-DM methodology, select appropriate machine learning algorithms, and enhance model construction and performance.
- Use RapidMiner to estimate and project values, and utilize analytical tools for time series forecasting.
14 hours
This instructor-led, live training in the UK (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.
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.
21 hours
In this instructor-led, live training in the UK, 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.
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.
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.
21 hours
This instructor-led, live training in the UK (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.
28 hours
In this instructor-led, live training in the UK, 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.
14 hours
Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.
This instructor-led, 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 real-world use cases such building a song recommendation system for music lovers
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
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.
14 hours
What will cities look like in the future? How can Artificial Intelligence (AI) be used to improve city planning? How can AI be used to make cities more efficient, livable, safer and environmentally friendly?
In this instructor-led, live training (onsite or remote), we examine the various technologies that make up AI, as well as the skill sets and mental framework required to put them to use for city planning. We also cover tools and approaches for gathering and organizing relevant data for use in AI, including data mining.
Audience
- City planners
- Architects
- Developers
- Transportation officials
- Part lecture, part discussion, and a series of interactive exercises.
- To request a customized training for this course, please contact us to arrange.
14 hours
AI is a collection of technologies for building intelligent systems capable of understanding data and the activities surrounding the data to make "intelligent decisions". For Telecom providers, building applications and services that make use of AI could open the door for improved operations and servicing in areas such as maintenance and network optimization.
In this course we examine the various technologies that make up AI and the skill sets required to put them to use. Throughout the course, we examine AI's specific applications within the Telecom industry.
Audience
- Network engineers
- Network operations personnel
- Telecom technical managers
- Part lecture, part discussion, hands-on exercises
35 hours
This course is created for people who have no previous experience in probability and statistics.
14 hours
Linear algebra is a branch of mathematics that deals with vectors, matrices, and linear transforms. Knowledge of linear algebra helps engineers and developers improve their machine learning capabilities. Understanding linear algebra concepts allows them to better understand the principles behind machine learning techniques and thus solve problems faster.
In this instructor-led, live training, participants will learn the fundamentals of linear algebra as they step through solving a machine learning problem using linear algebra methods.
By the end of this training, participants will be able to:
- Understand fundamental linear algebra concepts
- Learn the linear algebra skills needed for machine learning
- Use linear algebra structures and concepts when working with data, images, algorithms, etc.
- Solve a machine learning problem using linear algebra
- Developers
- Engineers
- Part lecture, part discussion, exercises and heavy hands-on practice
- To request a customized training for this course, please contact us to arrange.
21 hours
This instructor-led, live training in the UK (online or onsite) is aimed at engineers who wish to use Azure ML's drag-and-drop platform to deploy Machine Learning workloads without having to purchase software and hardware and without having to worry about maintenance and deployment.
By the end of this training, participants will be able to:
- Write highly-accurate machine learning models using Python, R, or zero-code tools.
- Leverage Azure's available data sets and algorithms to train and track machine learning and deep-learning models.
- Use Azures interactive workspace to collaboratively develop ML models.
- Choose from different Azure-supported ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
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.
28 hours
This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course.
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.
21 hours
This instructor-led, live training in the UK (online or onsite) is aimed at data scientists and developers who wish to create and train machine learning models for deployment into production-ready hosting environments.
By the end of this training, participants will be able to:
- Use notebook instances to prepare and upload data for training.
- Train machine learning models using training datasets.
- Deploy trained models to an endpoint to create predictions.
14 hours
This instructor-led, live training in the UK (online or onsite) is aimed at data scientists who wish to use Azure Machine Learning to build end-to-end machine learning models for predictive analysis.
By the end of this training, participants will be able to:
- Build machine learning models with zero programming experience.
- Create predictive algorithms with Azure Machine Learning.
- Deploy production ready machine learning algorithms.
21 hours
This course will be a combination of theory and practical work with specific examples used throughout the event.
Last Updated:
Upcoming Machine Learning (ML) Courses
Matlab for Deep Learning
2023-12-07 09:30
Manchester
14 hours
T2T: Creating Sequence to Sequence Models for Generalized Learning
2023-12-21 09:30
Manchester
7 hours
Advanced Machine Learning with R
2024-01-04 09:30
Manchester
21 hours
OpenFace: Creating Facial Recognition Systems
2024-01-18 09:30
Manchester
14 hours
Advanced Machine Learning with Python
2024-02-01 09:30
Manchester
21 hours
Advanced Machine Learning with Python
2024-02-15 09:30
Manchester
21 hours
T2T: Creating Sequence to Sequence Models for Generalized Learning
2024-02-29 09:30
Manchester
7 hours
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