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. 📢The course will be launched on 20.06.2022.
WHAT’S INSIDE?
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
Quizzes
Certificate
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 (preview)
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 (preview)
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 (preview)
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.
PREVIEW
⭐⭐⭐⭐⭐
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!
💯💯💯💯💯
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!
THERE IS MORE!
ML.NET Full-Stack Video Course is designed to help you become Machine Learning Superhero
BONUS #1
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.
BONUS #2
We got your back
SUPPORT Discord
Community
Private Discord Server in which all students can ask questions and connect with like-minded people.
About the AUTHOR
From Developer to Developers with love
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.
