ML.NET Full-Stack

Complete Guide to Machine Learning for .NET Developers! From the basics of machine learning, to more complex topics like neural networks, object detection and NLP, this course will guide you into becoming ML.NET superhero. 


Through 13 chapters, you will learn how to run small ML.NET experiments with Interactive Notebooks, how to build large projects with ML.NET in Visual Studio and how to apply AutoML to a variety of machine learning problems.

Hours of video

Reading Materials



Basics of Machine Learning

In the first section of this course, we cover some basic topics of machine learning. We define the problems machine learning is trying to solve, and we define the types of machine learning. Apart from that, we talk about the anatomy of machine learning algorithms and learn about performance metrics that we use throughout this course.

Regression, Classification and Clustering Algorithms
The majority of chapters of the course are split into three parts: Intuition & Math, Experiments with Interactive Notebook and Project in Visual Studio. This is done for every type of machine learning algorithm in ML.NET. Check out the example of how it is done for Linear Regression.
Decision Trees and Regression Forest

What about algorithms that can perform both classification and regression. We got you covered! In this course, we explore Decision Trees, Random Forest and SVM, using the same approach.

AutoML with Model Builder

Learn how to automate the training process with ML.NET Model Builder.

Recommendation Systems

From Netflix, Google, and Amazon, to smaller webshops, recommendation systems are everywhere. In fact, this type of system represents probably one of the most successful business applications of Machine Learning. Their ability to predict what users would like to read, watch and buy proved to be good not only for the business but for the users as well.

Image Classification and Object Detection

Computer Vision is a field that was completely changed with the rise of Deep Learning and Neural Networks. In this course, you will learn how to use ML.NET for image recognition and object detection with YOLO.

NLP with BERT and Sentiment Analysis

Several chapters in this course cover NLP problems. The most popular neural networks for NLP – Transformers are thoroughly explained. Two problems that are explored in this course – Sentiment Analysis and Q&A with BERT. Learn what that looks like in this preview.



Interesting DEEP complete guide/class for Machine Learning with a focus on .NET developers.

Scott Hanselman

.NET Teacher, Speaker, Podcast Host, Microsoft



ML.NET is built for .NET developers to get them closer to machine learning. Since 2018, ML.NET is evolving into much more than just a machine learning library, it is evolving into a comprehensive framework that leverages its own features as well as other libraries and runtimes.

Nikola has managed to put them all together into an ultimate guide that would be beneficial for all .NET developers.

Zeljko Todorovic

Senior Software Engineer, Microsoft


As a .NET developer, I always felt I was missing out on Machine Learning because tools for it are usually built in other languages. This course changed that feeling completely! I especially liked that this course goes beyond the plain use of ML.NET, but covers details of Machine Learning Algorithms and helps you understand them. Big recommendation!

Marinko Spasojevic

CTO and Author, CodeMaze


ML.NET Full-Stack Video Course is designed to help you become Machine Learning Superhero


Just enough math to make you dangerous

Mathematics for Machine Learning

The most common question that I get at meetups and conferences is: “How much math should I know?”. This guide got you covered!

While some people will argue that even this much math is too much, in my humble opinion, knowing this bare minimum will help you understand concepts of machine learning and AI in more depth.


We got your back


Private Discord Server in which all students can ask questions and connect with like-minded people.


Great material! Nikola explains nicely and his voice is easy to follow. The course is well organized and the lecturer shows a deep understanding of the field.

Following the course, you can see that Nikola really enjoys while he is sharing knowledge. That makes this course special and different! Tasks and theory cover everything you need to become an expert yourself. I warmly recommend this course!

Kristina Licenberger

NLP Engineer, Vega IT


An excellent comprehensive course for machine learning! Although I’m not a novice, this course helped me to improve my knowledge from general concepts to intricate details of ML for .NET. I certainly recommend it!

Nemanja Kovacev, MD, PhD

AI in Healthcare Specialist

About the AUTHOR

From Developer to Developers with love

Nikola Živković is a software developer with over 10 years of experience in the industry. He’s earned a Master’s degree in Computer Science from the University of Novi Sad in 2011, but by then he had already been working for several companies. During the time span in the industry, he has worked on large enterprise systems, as well as on small web projects.

In the past couple of years he has been specializing in Data Science (Machine Learning and Deep Learning to be precise) and his goal is to unite this knowledge together with some best traditional programming practices.

He is also experienced as a speaker and author, talking at meet-ups and conferences, and as a guest lecturer at the University of Novi Sad. You can find his online courses on Pack Publishing and Educative. 


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