The Art Of Machine Learning: A Hands-On Guide to Machine Learning with R

The Art of Machine Learning provides a practical approach to machine learning, requiring only an intuitive grasp of charts, graphs, and the slope of a line, and familiarity with the R programming language. It covers a range of machine learning methods, from k-Nearest Neighbours to neural networks, and introduces text and image classification and time series.

Format: Paperback / softback
Length: 272 pages
Publication date: 09 January 2024
Publisher: No Starch Press,US

Machine learning is a powerful tool that allows us to analyze and understand data, making predictions and taking informed decisions. However, many people are intimidated by the idea of machine learning because they believe that it requires advanced math skills. In reality, machine learning can be accessible to anyone with a basic understanding of math and a willingness to learn.

This book is designed to provide a comprehensive and practical introduction to machine learning without requiring advanced math skills. It will cover the basics of machine learning, including supervised and unsupervised learning, neural networks, and decision trees. You will learn how to use popular machine learning libraries such as TensorFlow and Scikit-Learn, and you will apply machine learning techniques to real-world data sets.

One of the key strengths of this book is its emphasis on practical applications. You will not only learn how to implement machine learning algorithms, but you will also learn how to evaluate and improve the performance of your models. You will learn how to identify and address common problems that can arise in machine learning, such as overfitting and bias.

In addition to its practical focus, this book also provides a strong foundational understanding of machine learning principles. You will learn about the different types of machine learning algorithms, including regression, classification, and clustering. You will learn about the importance of data preprocessing and feature engineering, and you will learn about the challenges and limitations of machine learning.

Whether you are a data scientist, a business analyst, or a student interested in learning more about machine learning, this book is for you. It will provide you with the skills and knowledge you need to start using machine learning in your own work.

So, if you are ready to dive into the world of machine learning, but you are intimidated by the idea of advanced math, this book is for you. It will provide you with the practical skills and foundational knowledge you need to succeed.

Dimension: 235 x 178 (mm)
ISBN-13: 9781718502109