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. Onsite live Machine Learning training can be carried out locally on customer premises in Kazakhstan or in NobleProg corporate training centers in Kazakhstan.
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Testimonials
Convolution filter
Francesco Ferrara - Inpeco SpA
Course: Introduction to Machine Learning
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
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
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
Keeping it short and simple. Creating intuition and visual models around the concepts (decision tree graph, linear equations, calculating y_pred manually to prove how the model works).
Nicolae - Oana Pancea , DB Global Technology
Course: Machine Learning
It helped me achieve my goal of understanding ML. Much respect for Pablo for giving a proper introduction in this topic, since it becomes obvious after 3 days of training how vast this topic is. I have also enjoyed A LOT the idea of virtual machines you have provided, which had very good latency! It allowed every coursant to do experiments at their own pace.
Silviu - Oana Pancea , DB Global Technology
Course: Machine Learning
Pablo is a great trainer! His teaching style is fun and easy to follow. He explained complex topics in an understandable way and took time to go through details and offer practical examples. He answered every question, even those outside the general scope of the course. He prepared examples, exercises, learning materials and managed to fit a large amount of information in just 3 days. Would definitely participate in another course taught by him.
Oana Pancea , DB Global Technology
Course: Machine Learning
The vibe, the knowledge and the attitude of trainor
Oana Pancea , DB Global Technology
Course: Machine Learning
The in-depth detail the instructor went into with the subject matter.
Maureen Barber, Telephonics Corp
Course: Machine Learning
The way practical part, seeing the theory materializing into something practical is great.
Lisa Fekade - Vodacom
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
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
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
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
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
the hands-on exercise and the instructor seem very knowledgeable.
Ashok Nair, City of Calgary
Course: Machine Learning with Python – 4 Days
The trainer was a practitioner with a lot of experience and had a very good knowledge of the material.
Witold Iwaniec - Ashok Nair, City of Calgary
Course: Machine Learning with Python – 4 Days
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
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
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
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
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
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
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
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
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
Szkolenie rewelacyjne, jedno z najlepszych, na jakich bylem! Prowadzacy Rafal doskonale odpowiadal w zakresie poruuszanych zagadnien, bardzo dokladnie tlumaczyl wszystkie metody. Jestem bardzo zadowolony i chetnie ponownie skorzystam ze szkolenia prowadzonego przez tego szkoleniowca.
Darek Paszkowski - Danuta Haber, Orange Szkolenia Sp. z o.o.
Course: Feature Engineering for Machine Learning
Materiał był przekazywany w sposób bardzo zrozumiały dla wszystkich uczestników
Danuta Haber, Orange Szkolenia Sp. z o.o.
Course: Feature Engineering for Machine Learning
Rysunki na flipcharcie, całe szkolenie.
Kasia Nawrot - Danuta Haber, Orange Szkolenia Sp. z o.o.
Course: Feature Engineering for Machine Learning
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 pace was really good. I never felt behind, and never really went ahead of the trainer. The overall structure was quite good apart from the improvements mentioned above. Also, very clean explanation of the topics.
Edina Kiss, Accenture Industrial SS
Course: Azure Machine Learning (AML)
It was overall nice. Funny and knowledgeable trainer, explaining what actually matters.
Edina Kiss, Accenture Industrial SS
Course: Azure Machine Learning (AML)
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)
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 intuition part of machine learning models.
International Golden Group PJSC
Course: Fundamentals of Artificial Intelligence and Machine Learning
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 in Kazakhstan
Machine Learning (ML) Course Outlines in Kazakhstan
Course Name
Duration
Overview
Course Name
Duration
Overview
21 hours
This instructor-led, live training in Kazakhstan (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 Kazakhstan (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.
7 hours
This instructor-led, live training in Kazakhstan (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.
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.
21 hours
This instructor-led, live training in Kazakhstan (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.
7 hours
This instructor-led, live training in Kazakhstan (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 Kazakhstan (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 Kazakhstan (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 Kazakhstan, 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 Kazakhstan (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 Kazakhstan, 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.
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.
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.
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.
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.
21 hours
This course will be a combination of theory and practical work with specific examples used throughout the event.
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.
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.
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.
35 hours
This course is created for people who have no previous experience in probability and statistics.
7 hours
The Wolfram System's integrated environment makes it an efficient tool for both analyzing and presenting data. This course covers aspects of the Wolfram Language relevant to analytics, including statistical computation, visualization, data import and export and automatic generation of reports.
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.
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
7 hours
Snorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain.
In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.
By the end of this training, participants will be able to:
- Programmatically create training sets to enable the labeling of massive training sets
- Train high-quality end models by first modeling noisy training sets
- Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Encog is an open-source machine learning framework for Java and .Net.
In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models.
By the end of this training, participants will be able to:
- Implement different neural networks optimization techniques to resolve underfitting and overfitting
- Understand and choose from a number of neural network architectures
- Implement supervised feed forward and feedback networks
- Developers
- Analysts
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
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