Fundamental Mathematical Concepts for Machine Learning in Science, 2024th Edition - Umberto Michelucci

This book, titled “Fundamental Mathematical Concepts for Machine Learning in Science, 2024th Edition,” has a vision of helping science lovers understand how machine learning can be applied in natural sciences. This site is good for breaking down complicated thoughts in mathematics and helps everyone, whether they’re good at math or not. It does not matter if you are a newbie in sports or a professional sportsman; you will get much from it.

This is not a book on how to apply algorithms; it is a book that speaks about how machine learning works at its basic core. It includes such activities as creating datasets, verifying the model’s accuracy, and identifying optimal algorithm parameters. This paper presents the theory, which, when explained to these scientists, equips them with the powerful methods that they need when applying machine learning to their research.

This is not a reference book of one thousand problems and solutions for machine learning in science. This book on Fundamental Mathematical Concepts for Machine Learning in Science is designed to help readers focus on the big picture rather than the minutiae of particular techniques. It shows how calculus helps in training models and how linear algebra enhances the operation of algorithms. For every idea on the page, you learn how it applies to the grand scheme of things, allowing you to apply machine learning in your scientific endeavours confidently.

Fundamental Mathematical Concepts for Machine Learning in Science, 2024th Edition Table of Contents:

1. Chapter 1: Introduction
2. Chapter 2: Machine Learning: History and Terminology
3. Chapter 3: Calculus and Optimization for Machine Learning
4. Chapter 4: Linear Algebra
5. Chapter 5: Statistics and Probability for Machine Learning
6. Chapter 6: Sampling Theory (a.k.a. Creating a Dataset Properly)
7. Chapter 7: Model Validation and Selection
8. Chapter 8: Unbalanced Datasets and Machine Learning Metrics
9. Chapter 9: Hyper-parameter Tuning
10. Chapter 10: Feature Importance and Selection

Who is this course for?

1. Researchers in the fields of natural sciences
2. The ones who have an interest in the structural implementation of machine learning in their research

Click on the links below to Download Fundamental Mathematical Concepts for Machine Learning in Science, 2024th Edition!