INTRODUCTION TO MACHINE LEARNING SUPERVISED VS. UNSUPERVISED LEARNING: Key differences between supervised and unsupervised learning methods, with examples of each

By Renata Sloane

Introduction to Machine Learning: Supervised vs. Unsupervised Learning is your entryway into understanding two of the most fundamental approaches in machine learning. Whether you're just starting out in the field or seeking to solidify your understanding, this book provides a clear and concise explanation of the key differences between supervised and unsupervised learning methods. Supervised learning and unsupervised learning are two crucial paradigms that define how algorithms learn from data. In this book, you will explore supervised learning, where algorithms are trained using labeled data, and unsupervised learning, where algorithms attempt to find hidden patterns or intrinsic structures in data without labeled outputs. Through detailed explanations and real-world examples, this guide will help you understand when to apply each approach, how they differ in terms of data requirements, and the types of problems they can solve. You'll dive into popular supervised learning algorithms such as linear regression, decision trees, and support vector machines, alongside unsupervised learning methods like k-means clustering and hierarchical clustering. By the end of this book, you will be well-equipped to recognize which learning method is best suited for your projects and how to implement them in your machine learning workflows.

• Format: Hardcover
• Language: English
• ISBN: B0F5MC6M9F
• ISBN13: 9798280431980
• Release Date: April 2025
• Publisher: Independently published
• Length: 196 Pages
• Dimensions: 9.0" x 0.6" x 6.0"