Repository for Machine Learning resources, frameworks, and projects. Managed by the DLSU Machine Learning Group. - dlsucomet/MLResources

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 …

To build and program intelligent machines, you must first understand classical statistics. Algorithms derived from classical statistics contribute the metaphorical blood cells and oxygen …

Chapter 1 Introduction to Machine Learning 1 What Is Machine Learning? 2 What Problems Will Machine Learning Be Solving in This Book? 3 Classification 4 Regression 4 Clustering 5 …

Implementing some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator, and making it better.

What follows next are three Python machine learning projects. They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give …

This book is your practical guide to moving from novice to master in machine learning (ML) with Python 3 in six steps. The six steps path has been designed based on the “six degrees of …

Figure 3: Categories of machine learning (Source: Raschka & Mirjalili: Python Machine Learning, 3rd Ed.). 1.3.1 Supervised Learning Supervised learning is the subcategory of machine …

Jul 19, 2023 · You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the …

Dec 12, 2019 · Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference …