This book is for current and aspiring machine learning practitioners looking to implement solutions to real-world machine learning problems. This is an introduc‐tory book requiring no previous knowledge …

For this book, all of the examples are tested using Python 3 and the Scikit-learn library, a Python library that implements the various types of machine learning algorithms, such as classification, regression, …

Repository for Machine Learning resources, frameworks, and projects. Managed by the DLSU Machine Learning Group. - MLResources/books/ [ML] Introduction to Machine Learning with Python …

A problem with machine learning, especially when you are starting out and want to learn about the algorithms, is that it is often difficult to get suitable test data.

Welcome to the second edition of Machine Learning Engineering with Python, a book that aims to introduce you to the exciting world of making machine learning (ML) systems production-ready.

"Machine Learning with Python" by G. R. Liu provides a comprehensive introduction to the essential concepts, theories, computational techniques, and applications of machine learning.

Happy Coding with Python!

Machine learning (ML) can be broadly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning. Each type of learning has its own characteristics and is applied …

This book of Python projects in machine learning tries to do just that: to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning to …

This tutorial explores the use of Python for machine learning, detailing various libraries such as NumPy, SciPy, Scikit-Learn, and Matplotlib. It discusses essential machine learning concepts, provides …