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Books To Read To Start Your ML Journey

Books To Read To Start Your ML Journey

  • As a newbie, these books can get you started in your machine learning journey

One of the most exciting fields to be in right now is machine learning. But starting your journey there can be quite intimidating at first. With the internet containing so much information, the amount of content can be overwhelming for someone, especially at the initial stages of learning. Getting access to the right kind of resources when one is starting out sets the foundation right for growing in the domain.

Here is the list of books that you should read as a beginner just starting out in machine learning:

Machine Learning For Absolute Beginners: A Plain English Introduction by Oliver Theobald

This is a good book as an introductory text to machine learning. It teaches you how to download data sets and what kind of tools and ML libraries one needs. It introduces you to data scrubbing techniques, including one-hot encoding, binning and dealing with missing data, preparing data for analysis, including k-fold validation, regression analysis to create trend lines, and clustering. The book also contains the basics of neural networks, decision trees, and bias/variance. It does not require prior coding experience to understand the concepts of the book.

Machine Learning for Dummies by John Paul Mueller, Luca Massaron

This book is written by two data scientists and introduces anyone who wants to use machine learning techniques for practical tasks. It makes the reader understand the meaning of programming languages and the tools needed to make ML-based turns work in reality. It also helps comprehend how daily activities are powered by machine learning and introduces R and Python to perform pattern-oriented tasks and data analysis.

Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller, Sarah Guido

Even if one uses Python as a beginner, the book will help the reader build machine learning solutions. The reader will learn about the basic concepts and applications, the advantages and pitfalls of popularly used machine learning algorithms, and how to represent data processed by machine learning. This will include which aspects of data to focus on, advanced methods for model evaluation and parameter tuning, pipelines for chaining models and encapsulating the workflow and methods for working with text data, including text-specific processing techniques and suggestions for improving your machine learning and data science skills.

Machine Learning in Action by Peter Harrington

This book is a good start for newcomers to machine learning. It contains topics starting with ML basics, classifying with k-nearest neighbours, splitting datasets one feature at a time, decision trees, logistic regression, tree-based regression, using principal component analysis to simplify data, simplifying data with the singular value decomposition and big data and MapReduce. Most of the examples use Python; hence, familiarity in Python will be desirable.

The book is for developers and does not use academic language but takes the reader through techniques used in daily work. It contains examples in Python that bring out the core algorithms of statistical data processing, data analysis, and data visualization in code that one can reuse.

See Also

The Hundred Page Machine Learning Book – Andriy Burkov

It is a popular choice among machine learning enthusiasts. A newbie in machine learning will find this book comfortable to comprehend, setting the scene for their machine learning journey. Experienced people will use this book as a collection of pointers to the directions of further self-improvement. It comes with a wiki that contains pages that extend some book chapters with additional information, Q&A, code snippets, further reading, tools, etc.

An Introduction to Statistical Learning with Applications in R – Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

This book gives a good start to someone interested in the field of statistical learning. It includes topics like linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering while citing real-world examples. Each chapter contains a tutorial on implementing the analyses and methods shown in R. It is a combined work of a group of authors with experience teaching machine learning and working with predictive analysis.

If you desire to enter the exciting field of machine learning and build algorithms, these books can act as a stepping stone in your journey.

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