Artificial intelligence and machine learning are expanding disciplines and areas of research. Although the sophisticated applications of machine learning that we read about in the media may seem daunting and unapproachable, the fundamental ideas are very simple to understand. In this post, we’ll examine some of the most well-liked Machine Learning Books for machine learning novices. Math and some coding language knowledge are prerequisites for several of these publications, but we’ll make note of that where applicable.
While there are many Top Machine Learning Books available nowadays, these picks are particularly helpful for those who are just starting out in the subject. The majority of these provide an introduction or an overview of machine learning via the prism of a particular subject area, such as statistics, case studies, and algorithms, or for those who are already familiar with Python.
Some Popular Machine Learning Books
1. Machine learning – 4 books in 1 by Samuel Hack
Machine Learning: 4 Books in 1 is a comprehensive manual for novices to learn the fundamentals of Python programming and comprehend how to use data science to create artificial intelligence. Overview of Machine Learning, Beginning Python Programming, Beginning Data Science, and Artificial Intelligence for Beginners are the four volumes that make up this book.
It covers all the topics you need to know about machine learning, such as feature engineering, model selection, regression, and classification, supervised and unsupervised learning, and more. This book will help you rapidly grasp the fundamentals of machine learning and begin developing your own AI applications with its concise explanations and useful examples.
Available on: Amazon, AbeBooks.
Pricing:
- Kindle Edition: Free for Prime members.
- Paperback: $12.38.
- Hardcover: $42.49.
2. Mathematics for Machine Learning by Marc Peter Deisenroth
To create better models and algorithms, Mathematics for Machine Learning is one of the Machine Learning Books that assist you in comprehending the mathematical underpinnings of machine learning.
It addresses subjects including probability, statistics, optimization, and linear algebra. With the help of this book, you will be able to comprehend the mathematics essential to creating machine-learning models and algorithms.
If you want to brush up on the mathematics required for machine learning, this book is an excellent resource. It is quite succinct, yet it still has enough information to let readers know what the key points are. If you need to review certain ideas or refresh your memory on my expertise in general, here is the place to go.
Available on: Amazon, Cambridge University Press.
Pricing:
- E-book: $47.49.
- Paperback: $44.99.
- Hardcover: $73.03.
3. Machine Learning For Dummies by John Paul Mueller and Luca Massaron
Even though we’re talking about “absolute beginners,” the well-known “Dummies” series is a helpful place to start. The goal of this one of the Top Machine Learning Books is to familiarize readers with the fundamental ideas and theories of machine learning and its practical applications. It explains how to put the theoretical study of machine learning into practice by outlining the core languages and tools required for the field.
The book gives a brief introduction to Python and R coding, which is used to train computers to recognize patterns and evaluate data. We may infer the applications of machine learning in our everyday lives—web searches, online advertisements, email filters, fraud detection, and so forth—from those little actions and patterns. You may dabble in the field of machine learning with this book.
Available on: Amazon, Dummies.
Pricing:
- Kindle Edition: $21.
- Paperback: $20.49.
4. Linear algebra for Machine learning by Charu C. Aggarwal
Two essential subjects in machine learning, optimization, and linear algebra, are thoroughly introduced in this textbook. It is appropriate for those with little to no experience in mathematics and covers both theory and applications.
Before delving into more complex subjects like matrix decompositions, eigenvalues and eigenvectors, singular value decomposition, and least squares techniques, the book starts with a review of fundamental linear algebra. Other optimization techniques that follow include gradient descent, interior point methods, conjugate gradient methods, and Newton’s Method.
Available on: Amazon, Bookswagon.
Pricing:
- E-book: $15.
- Hardcover: $40.36.
- Paperback: $45.10.
5. Programming Collective Intelligence by Toby Segaran
This is not so much an introductory Machine Learning Book as it is a useful field guide for using machine learning. This book will teach you how to build machine-learning algorithms that collect data relevant to certain tasks.
It instructs users on how to write programs that get data from websites, gather data from apps, and deduce the meaning of the data after it has been gathered. “Programming Collective Intelligence” also includes examples of search engine algorithms, filtering strategies, group or pattern detection techniques, prediction approaches, and more. Exercises that demonstrate the teachings in practice are included in every chapter.
Available on: Amazon, WorldCat.
Pricing:
- Kindle Edition: $24.55.
- Paperback: $25.84.
6. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
Without being too simplistic, it is useful for beginning programmers. For serious practice, I think it’s a great choice. You’ll get a lot of knowledge by reading this book slowly. Although students have noted that the print quality varies, they still think the book is well worth the investment due to the high quality of its material.
Numerous ML topics are covered in this book, and they are all accompanied by excellent examples. But for a newbie, it should be challenging enough. Also, it doesn’t include mathematical explanations or in-depth coverage of complex themes, thus it may not be the ideal book for an experienced reader. If you have a decent fundamental knowledge of ML and are searching for real-life examples with Python code, it is an amazing book.
Available on: Amazon, Bookswagon.
Pricing: $56.
7. Machine Learning for Hackers by Drew Conway and John Myles White
In this context, the term “hackers” refers to programmers who collaborate to hack code for practical tasks and particular objectives. “Machine Learning for Hackers” is a helpful resource for those who lack a strong background in mathematics but have familiarity with programming and coding languages. Because machine learning requires complex algorithms to analyze data, a lot of seasoned programmers may not have the necessary math background.
Rather than focusing mostly on mathematical theory, the book presents the subject in real-world practical applications via interactive case studies. It explains common machine learning issues and how to use the R programming language to address them.
Machine learning has many uses, ranging from comparing U.S. Senators based on their voting histories to creating a Twitter recommendation system for users to follow to identifying spam emails based on the content of the email.
Available on: Amazon, Goodreads.
Pricing:
- Kindle Edition: $30.99.
- Paperback: $34.41.
8. Machine Learning for Absolute Beginners by Oliver Theobald
Machine learning is only simple if you have the proper mentor and Top Machine Learning Books. The majority of us overlook the significance of basic ideas that clarify more sophisticated ones. Oliver Theobald’s ‘Machine Learning for Absolute Beginners’ is the foundational reference book that I advise utilizing as a result.
This book teaches machine learning from the beginning and explains concepts in clear terms for the reader to understand. Those looking to pursue a career in machine learning may gain just as much from this book as non-technical readers would. It also includes helpful resources that anybody wishing to study like an expert might use.
Available on: Amazon, Goodreads.
Pricing:
- Free Kindle edition with prime membership.
- Paperback: $15.99.
- Hardcover: $25.
Top Machine Learning Courses
AI and ML are the new future. If you are enthusiastic about ML (Machine Learning), then you need more than just Machine Learning Books. You need a proper course on ML. You can consider these courses:
a. Machine Learning.
- Provided by: Coursera.
- Pricing: Free to enroll, $79 for Certificate.
b. Deep Learning Specialization.
- Provided by: Coursera.
- Pricing: Free to enroll, $49/month for Certificate.
c. Machine Learning Crash Course.
- Provided by: Google AI.
- Pricing: Free.
d. Machine Learning with Python.
- Provided by: Coursera.
- Pricing: Free to enroll, $39/month for Certificate.
e. Machine Learning.
- Provided by: edX.
- Pricing: Free to enroll, $300 for Certificate.
f. Introduction to Machine Learning for Coders.
- Provided by: Fast.ai.
- Pricing: Free.
g. Data Science and Machine Learning Program.
- Provided by: Scaler.
- Pricing: $3,000.
h. Machine Learning.
- Provided by: HarvardX.
- Pricing: Free to enroll, $149 per month for certificate.
FAQ
Q: Which library is better for machine learning?
A: Based on NumPy and SciPy, Scikit-learn is a widely used machine learning package. In addition to being useful for data mining, modeling, and analysis, it supports the majority of the traditional supervised and unsupervised learning techniques.
Q: For machine learning, how many records are required?
A: For the most basic machine learning algorithms, 1000 samples per category is thought to be the least, although in most circumstances, this won’t be sufficient to fix the issue. The more training data you have, the more difficult the issue is. The quantity of data samples needs to correspond with the quantity of parameters.
Q: What language is most often used in machine learning?
A: A crucial language for data analytics and machine learning is Python.