Machine Learning Training Courses

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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. The US onsite live Machine Learning trainings can be carried out locally on customer premises or in NobleProg corporate training centers.

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Machine Learning Subcategories

Deep Learning Training CoursesDeep Learning
RapidMiner Training CoursesRapidMiner
Apache SystemML Training CoursesApache SystemML
Pattern Recognition Training CoursesPattern Recognition
PredictionIO Training CoursesPredictionIO
AutoML Training CoursesAutoML
H2O Training CoursesH2O
DataRobot Training CoursesDataRobot
ML Kit Training CoursesML Kit
Random Forest Training CoursesRandom Forest
AdaBoost Training CoursesAdaBoost
Weka Training CoursesWeka

ML (Machine Learning) Course Outlines

Course Name
Duration
Overview
Course Name
Duration
Overview

Generative AI with Large Language Models (LLMs)

21 hours
This instructor-led, live training in the US (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.

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 (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

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

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.

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.

Introduction to Large Language Models (LLMs)

14 hours
This instructor-led, live training in the US (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.

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 (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.

Advanced Analytics with RapidMiner

14 hours
This instructor-led, live training in the US (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.

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 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

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.

Artificial Intelligence (AI) for City Planning

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
Format of the Course
  • Part lecture, part discussion, and a series of interactive exercises.
Note
  • To request a customized training for this course, please contact us to arrange.

AI Awareness for Telecom

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
Format of the course
  • Part lecture, part discussion, hands-on exercises

From Zero to AI

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

Algebra for Machine Learning

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
Audience
  • Developers
  • Engineers
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Note
  • To request a customized training for this course, please contact us to arrange.

Azure Machine Learning (AML)

21 hours
This instructor-led, live training in the US (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.

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.

Applied AI from Scratch

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. 

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.

Amazon Web Services (AWS) SageMaker

21 hours
This instructor-led, live training in the US (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.

Azure Machine Learning

14 hours
This instructor-led, live training in the US (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.

Machine Learning

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

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Upcoming ML (Machine Learning) Courses

Amazon Web Services (AWS) SageMaker

12/21/2023 - 09:30
CT, East Berlin - East Berlin
21 hours

XGBoost for Gradient Boosting

01/04/2024 - 09:30
CT, East Berlin - East Berlin
14 hours

Dataiku for Enterprise AI and Machine Learning

01/18/2024 - 09:30
CT, East Berlin - East Berlin
21 hours

Fundamentals of Artificial Intelligence (AI) and Machine Learning

02/01/2024 - 09:30
CT, East Berlin - East Berlin
28 hours

Machine Learning and AI with ML.NET

02/15/2024 - 09:30
CT, East Berlin - East Berlin
21 hours

Machine Learning and AI with ML.NET

02/29/2024 - 09:30
CT, East Berlin - East Berlin
21 hours

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7 hours

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14 hours

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