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 knowledge of machine learning or artificial intelli‐ gence (AI). We focus on using Python and the scikit-learn library, and work

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 Types of Machine Learning Algorithms 5 Supervised Learning 5 Unsupervised Learning 7 Getting the Tools 8 Obtaining Anaconda 8 Installing Anaconda 9

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

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 practical aspects of using machine learning algorithms, rather than the math behind them.

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 you a background in deep reinforcement learning through building a bot for Atari.

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 separation” theory, which states that everyone and everything is a maximum of six steps away.

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 practical implementations of algorithms like decision trees, and guides through the process of evaluating algorithms with cross-validation.

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

We focus on using Python and the scikit-learn library, and work through all the steps to create a successful machine learning application. The meth‐ods we introduce will be helpful for scientists and researchers, as well as data scien‐tists working on commercial applications.