8 Machine Learning Books for Beginners: A 2023 Reading List

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Our lives are increasingly influenced by machine learning. This area of artificial intelligence (AI) enables chatbots, personalises your Netflix recommendations, and decides what appears in your TikTok stream. It significantly impacts healthcare technology, as machines can diagnose illnesses, prescribe medications, and even perform surgery.

The need for knowledgeable data scientists and machine learning engineers is increasing as more companies recognise the advantages of machine learning. Surprisingly, based on a 344 per cent increase in employment between 2015 and 2018, machine learning engineering was named the most excellent job in the US for 2019.

Books are an excellent method to get acquainted with the main ideas, jargon, and developments in machine learning if you're curious about the subject. A selection of machine learning books for beginners has been compiled by us, ranging from broad overviews to books that concentrate on particular topics like statistics, deep learning, and predictive analytics.

These books can help you to:

  • Assess your suitability for a job in machine learning.
  • Find your qualifications to become a data scientist or machine learning engineer.
  • Know how to search and prepare for job interviews
  • Keep up with the most recent developments in artificial intelligence and machine learning.

These books are among several good ones on machine learning and artificial intelligence that are particularly helpful for those just getting started in this discipline. Most of these provide an introduction or overview of machine learning through the perspective of a particular subject area, like case studies and algorithms, statistics, or those already familiar with Python. The following eight books are listed:

The Hundred-Page Machine Learning Book by Andriy Burkov

"The Hundred-Page Machine Learning Book" by Andriy Burkov is a concise introduction to machine learning for readers with some background in mathematics and programming. The book covers various topics, including supervised and unsupervised learning, deep learning, and natural language processing. It also includes practical advice on building and evaluating machine learning models.

The book is intended to provide a high-level overview of machine learning concepts and techniques rather than comprehensively treating the subject. It is well-suited for readers who want to understand machine learning and how it can be applied to solve real-world problems.

Overall, "The Hundred-Page Machine Learning Book" is a valuable resource for beginners looking to get started with machine learning and for more experienced practitioners who want to refresh their knowledge of the field.

Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy

"Fundamentals of Machine Learning for Predictive Data Analytics" by John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy is a textbook that introduces machine learning for predictive data analytics. It is suitable for readers with a background in mathematics and computer science. The book covers various topics, including supervised and unsupervised learning, deep learning, and natural language processing.

The book begins with an introduction to machine learning concepts and techniques and then goes on to cover a variety of machine learning algorithms and methods. It also includes chapters on model evaluation, data preprocessing, and working with large datasets.

Overall, "Fundamentals of Machine Learning for Predictive Data Analytics" is a comprehensive and well-written resource for anyone looking to learn about machine learning and how it can be applied to solve real-world problems. It is particularly well-suited for students and professionals who want to gain a solid foundation in the field.

Programming Collective Intelligence by Toby Segaran

"Programming Collective Intelligence" by Toby Segaran is a book that introduces machine learning and artificial intelligence concepts. It shows how to apply them to real-world problems using Python. The book covers various topics, including supervised and unsupervised learning, deep learning, and natural language processing. It is suitable for readers with some programming experience.

The book begins with an introduction to the basics of machine learning and artificial intelligence and then goes on to cover a variety of algorithms and techniques. It also includes chapters on data preprocessing, model evaluation, and working with large datasets.

Overall, "Programming Collective Intelligence" is a valuable resource for anyone interested in learning about machine learning and artificial intelligence. It is well-written and easy to understand, making it a good choice for beginners looking to get started in the field.

An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

"An Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani is a textbook that introduces statistical learning theory, focusing on linear models. It is suitable for students with a strong background in mathematics and statistics.

The book covers many topics, including linear regression, classification, and resampling methods. It also includes chapters on tree-based methods, support vector machines, and unsupervised learning. The book consists of several real-world examples, case studies, and exercises to help readers practice and apply the concepts covered.

Overall, "An Introduction to Statistical Learning" is a comprehensive and well-written resource for anyone looking to learn about statistical learning theory and its applications. It is particularly well-suited for students and professionals who want to gain a solid foundation in the field.

Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville is a textbook that provides a comprehensive introduction to deep learning, a subfield of machine learning focused on developing algorithms and models that can learn from data using multiple layers of artificial neural networks.

The book covers a wide range of topics, including supervised and unsupervised learning, convolutional neural networks, recurrent neural networks, and deep reinforcement learning. It also includes chapters on natural language processing, computer vision, and deep learning applications in various fields.

"Deep Learning" is a well-written and comprehensive resource for anyone interested in learning about deep learning and applications. It is suitable for readers with a background in mathematics and computer science. t is particularly suited for students and professionals to gain a solid foundation in the field.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Geron Aurelien

"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron is a book that provides a practical introduction to machine learning using Python. It covers various topics, including supervised and unsupervised learning, deep learning, and best practices for building and evaluating machine learning models.

The book begins with an introduction to machine learning concepts and techniques then goes on to cover a variety of algorithms and methods. It also includes chapters on data preprocessing, model evaluation, and working with large datasets. The book uses the sci-kit-learn Keras and TensorFlow libraries to demonstrate machine learning concepts and techniques and includes several real-world examples and case studies.

Overall, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" is a valuable resource for anyone interested in learning about machine learning and how it can be applied to solve real-world problems. It is suitable for readers with some programming experience. It is well-written and easy to understand, making it a good choice for beginners looking to get started in the field.

Machine Learning for Hackers by Drew Conway and John Myles White

"Machine Learning for Hackers" by Drew Conway and John Myles White is a book that introduces machine learning for readers with some background in programming and statistics. It covers various topics, including supervised and unsupervised learning, deep learning, and natural language processing. The book uses the R programming language to demonstrate machine learning concepts and techniques.

The book begins with an introduction to machine learning concepts and techniques and then goes on to cover a variety of algorithms and methods. It also includes chapters on data preprocessing, model evaluation, and working with large datasets. The book consists of several real-world examples, case studies, and exercises to help readers practice and apply the concepts covered.

Overall, "Machine Learning for Hackers" is a valuable resource for anyone interested in learning about machine learning and how it can be applied to solve real-world problems. It is suitable for readers with some programming and statistical knowledge. It is well-written and easy to understand, making it a good choice for beginners looking to get started in the field.

Machine Learning For Absolute Beginners Oliver Theobald

"Machine Learning For Absolute Beginners" by Oliver Theobald is a book that introduces machine learning to readers without prior knowledge of the subject. It covers various topics, including supervised and unsupervised learning, deep learning, and natural language processing. The book uses Python programming to demonstrate machine learning concepts and techniques.

The book begins with an introduction to machine learning concepts and techniques and then goes on to cover a variety of algorithms and methods. It also includes chapters on data preprocessing, model evaluation, and working with large datasets. The book consists of several real-world examples, case studies, and exercises to help readers practice and apply the concepts covered.

Overall, "Machine Learning For Absolute Beginners" is a valuable resource for anyone interested in learning about machine learning and how it can be applied to solve real-world problems. It is well-written and easy to understand, making it a good choice for beginners with no prior knowledge of the subject. It is suitable for readers with some programming experience.

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