Machine Learning Training Courses in Kazakhstan

Machine Learning Training Courses

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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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
Audience
  • Developers
  • Data scientists
Format of the course
  • 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
  1. Investors and AI entrepreneurs
  2. Managers and Engineers whose company is venturing into AI space
  3. 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
Audience
  • Developers
  • Data scientists
Format of the course
  • 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
Audience
  • Developers
  • Analysts
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice

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