Название: Machine Learning with Python: Foundations and Applications: ML, Volume 1
Автор: Mohammed Nurudeen
Издательство: Independently published
Год: 2024
Язык: английский
Формат: pdf, epub (true), azw3, mobi
Размер: 10.1 MB

This book, Machine Learning with Python: Foundations and Applications, is designed to offer a comprehensive introduction to Machine Learning using Python. The primary goal is to take readers from the fundamental concepts of Machine Learning to hands-on practical implementations using real-world examples. Python is the language of choice due to its extensive libraries, simplicity, and relevance in the Data Science community.

This book is divided into three main parts. The first volume focuses on understanding the basics of Machine Learning, key algorithms, and hands-on Python implementation.

At its core, Machine Learning is a branch of Artificial Intelligence (AI) that focuses on building systems capable of learning from data. Unlike traditional programming, where a human developer writes explicit rules for a machine to follow, Machine Learning systems can infer these rules by analyzing vast amounts of data. This allows machines to make decisions or predictions without being explicitly programmed for every situation. In other words, Machine Learning enables computers to recognize patterns, make predictions, and improve their performance over time through experience. Machine Learning is the study of computer algorithms that allow systems to learn from and make decisions based on data.

Algorithms are the mathematical models or methods used to process data and make predictions. Different types of Machine Learning problems require different algorithms. In this book, we’ll cover some of the most common Machine Learning algorithms, such as:

• Linear Regression
• Decision Trees
• k-Nearest Neighbors (k-NN)
• Support Vector Machines (SVM)
• Neural Networks

In this volume, we explore topics such as:

The theoretical foundation of Machine Learning
Different types of Machine Learning, including supervised, unsupervised, and reinforcement learning
Data preprocessing for Machine Learning tasks
An introduction to essential Machine Learning algorithms

Each chapter is carefully designed to build your knowledge step-by-step, ensuring that both beginners and those with some programming background can easily follow along.

Contents: