May 1, 2024
3 minute read
Machine learning (ML) is a subfield of artificial intelligence (AI) that gives computers the ability to learn without being explicitly programmed. ML algorithms are trained on data, and then they can make predictions or decisions based on new data. ML.NET is a cross-platform, open-source machine learning framework for .NET developers. It provides a set of tools and libraries that make it easy to build and deploy ML models.
Why Learn ML.NET?
There are many reasons why you might want to learn ML.NET. Here are a few:
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Find a path to becoming a ML.Net. Learn more at:
OpenCourser.com/topic/0sjbn5/ml
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
ML.Net.
Delves into the mathematical foundations of machine learning, aiming to empower readers with a deep understanding of the field.
Takes a probabilistic approach to machine learning, providing a deep understanding of the underlying mathematical foundations.
Provides a comprehensive treatment of deep learning, covering topics such as convolutional neural networks and recurrent neural networks.
Introduces machine learning using R, emphasizing practical applications.
Provides a comprehensive guide to machine learning using Python, covering essential concepts and algorithms.
Presents a practical, code-centric approach to machine learning, emphasizing real-world applications.
Offers a practical, no-nonsense guide to machine learning, focusing on real-world applications and ethical considerations.
Provides a comprehensive overview of machine learning algorithms, exploring their strengths and weaknesses.
Offers an introduction to reinforcement learning, exploring its concepts and algorithms.
Offers an approachable introduction to machine learning by focusing on the key concepts, including topics like supervised and unsupervised learning and how to build your own machine learning projects.
Offers a beginner-friendly introduction to machine learning, focusing on key concepts and applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/0sjbn5/ml
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