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 …

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

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.

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.

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

For those who do wish to look at the programming aspect of machine learning, Chapter 13 walks you through the entire process of setting up a supervised learning model using the popular programming …

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, …

Decision tree for predicting whether to bring an umbrella or not based on a record of 100 days. In a two-dimensional space, the dataset shown on the left is not separable. Mapping the dataset in a three …

We briefly discuss and explain different machine learning algorithms and real-world application areas based on machine learning. We highlight several research issues and potential future directions.

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 …