Best Machine Learning Books for Best Theories on Automation

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Best Machine Learning Books

Machine learning has come as a blessing to human civilization because it gives you the power to understand automation. Learning how to run machines helps up apply our bookish knowledge into everyday lives. It means that we can practice what the best machine learning books try to teach us in theory. Besides, the concept of machine learning is not restricted to something particular. It consists of vast range fields starting from digital marketing to space research.

Out of all other significant benefits of reading the best machine learning books, the most important is that we learn about the concept of AI (Artificial Intelligence). Besides, scientists are continuously working towards the development of all scientific inventions, including machines. Therefore, in this article, you will be able to learn about some of the books that can tell you about tools and intelligence in the modern-day.

Best machine learning books for experts and beginners

So, are you ready to explore the world of machine learning? Well, on the one hand, machine learning is indeed a problematic field. However, every complex study has an easy solution to it. There is, therefore, always a way of simplifying things. In this context, we have tried to curate a list of some of the best machine learning books that may interest you.

The Hundred Page Machine Learning Book

Do you think explaining the entire concept of machine learning is possible within a limit of a hundred pages? Well, this book is all about this confusion and more. Merely explaining, the book is one of the best machine learning books that has been endorsed by the best in the industry. The brains behind this book are Peter Norvig, who is the Research Director at Google. Another person who has shared his immense knowledge about the subject is Sujit Varakhedi, the Engineering Head of eBay.

After you finish reading the book, it is guaranteed that you will start understanding the details of Artificial Intelligence or AI. Later, you can appear for any ML oriented interview, and also start a new venture in this industry. However, The Hundred Page Machine Learning Book is not ideal for beginners. It is because the content that you get in it is a bit more than fundamental in terms of complexity.

What topics does it cover?

  • Anatomical study of algorithm
  • unsupervised and supervised learning
  • Fundamental algorithms
  • Various other types of learnings
  • Deep learning and neural networks

Basic facts:

  • author and publisher-Andriy Burkov
  • edition- first
  • Format- Leanpup(ebook)/ Paperback/ hardcover

Programming Collective Intelligence: Building Smart Web 2.0 Applications

It justifies a mention in this list because it is undoubtedly one of the best machine learning books that you will find. Toby Segaran wrote this book years back in 2007 before so much development of machine learning and data science happened. The author in this book uses Python as the essential language to deliver content to all the readers.

If we discuss the book in detail, you will find out that it is not an introductory level book for machine learning. Instead, the book will teach you the complexities that you may face while trying the implementation of machine learning. Moreover, you will be able to learn about the creation of algorithms.

Using such algorithms, you can quickly gather data from programs and applications. You can also access information from various websites using the same algorithms. In each of the chapters present in the book, you will learn how to improvise the algorithms. As a result, the efficiency of the algorithms increase, and they become more productive.

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What topics does it cover?

  • Bayesian filtering
  • Prediction procedures
  • The collaboration of filtering techniques
  • Vector machines support
  • Problem-solving in the field of intelligence evolution
  • Algorithms for search engines
  • Methods for finding patterns or groups
  • Positive matrix factorization

Basic facts:

  • Author- Toby Segaran
  • Format- Paperback/Kindle
  • Publisher- O/Reilly Media
  • Edition- first

Machine Learning for Hackers: Case Studies and Algorithms to Get You Started

The Machine Learning book that we will reveal now is specially created for senior programmers who now want to learn how to crunch data. In this context, hackers are not thieves of any kind. Instead, they are adept mathematicians who wish to enhance their knowledge in the field of machines.

The majority portion of the book talks about the complexities of analyzing data in the ‘R.’ Therefore, there is immense scope for people who want to learn more about this algorithm and have knowledge of ‘R.’ You will also be able to determine the various ways in which you can use ‘R’ to progress with data wrangling.

There are several concepts that you can polish after reading this book. Out of all the right parts, the highlights are the case studies of the different genres. From these, you will understand the importance of implementing machine learning with algorithms. The author believes that readers will realize practical examples more than deep arithmetical algorithms. So, that is precisely what you will get in this book in the form of excellent case studies. As a result, you start learning the concepts of ML effortlessly.

What topics does it cover?

  • Development of Bayesian classifier
  • Querying data using ‘R
  • Technique optimization
  • Linear regression

Basic facts:

  • Author- John Myles White & Drew Conway
  • Format- Paperback/Kindle
  • Publisher- O/Reilly Media
  • Edition- first

Machine Learning

From the name of the book, you must be already convinced that this is one of the best machine learning books, and your assumption is perfect. Machine Learning is a fantastic book that you can read as a beginner in this field. There are two significant benefits of reading this book, which includes learning about theorems as well as pseudocode summaries. Therefore, you are clear about the algorithms in a systematic manner.

Since you are a beginner, this book is designed primarily to make things easier for you. For example, there is an ample amount of case studies and examples that will make your learning experience more comfortable. Moreover, it will become easier for you to grab the complexities of the machine learning algorithms. Suppose you are already planning to make your passion your profession, this book is going to be of real help.

All the credit for comfort goes to the phenomenal narrative quality and the use of assignments. So, you keep learning more, and simultaneously the tasks will continuously enhance your skills. Therefore, Machine Learning is a perfect book that can be a part of any learning course or program.

What topics does it cover?

  • Genetic algorithms
  • Re-enforced learning
  • Logic programming induction
  • Techniques and concepts of machine learning
  • Learning basic approaches to ML

Basic facts:

  • Author- Tom M. Mitchell
  • Format- Paperback
  • Publisher- McGraw Hill Education
  • Edition- first

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

If you are someone who likes machine learning as well as statistics, then this book is precisely for you. Not only is this one of the best machine learning books in the market, but it is unique too. The specialty is that The Elements of Statistical Learning will help you to understand the underlying logic for the machine learning algorithms. It means that you learn machine learning in the language of Statistics.

You will find that the author has stressed highly on the importance of mathematical derivation to explain ML algorithms. Moreover, you must have the primary knowledge of Statistical calculations and Linear Algebra if you want to understand this book. It is factual that this book is only for experienced people. So, if you are trying to start a journey with machine learning, this book will come much later.

What topics does it cover?

  • Ensemble Learning
  • Unsupervised and Supervised learning
  • High-dimensional problems
  • Random Forests
  • Neural networks
  • Averaging and model inference

Basic facts:

  • Author- Robert Tibshirani, Trevor Hastie, and Jerom Friedman
  • Format- hardcover/ Kindle
  • Publisher- Springer
  • Latest Edition- Second

Learning from data- A Short Course

If you are in a little hurry and want to learn the primary details of machine learning, then Learning from data is an ideal option. Also, if you are keen on understanding core Engineering Mathematics, you may try this book. The author, in the book, stresses less on the advance concepts of machine learning. Instead, he wants to impart his knowledge of basic and complex concepts related to machine learning. Another good point about reading this book is that there are almost zero chances of getting bored. In this case, the reason is the format of the content in this book. Everything is virtually in pointers for someone to understand any problem. If you like the book, just go ahead and download online files that contain books of Yaser Abu Mustafa.

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What topics does it cover?

  • Noise and error
  • Validation
  • Kernel Methods
  • Overfitting
  • Vector machine support
  • Functions of Radial basis
  • Regularization

Basic facts:

  • Author- Malik Madgon-Ismail, Yaser Abu Mostafa, and Hsuan-Tien- Lin
  • Format- hardcover/ Kindle
  • Publisher- AMLBook
  • Latest Edition- First

Pattern Recognition and Machine Learning

As you can already understand from the title of the book, it teaches you about PR and ML. So, if you want to pursue statistics and relate it with pattern recognition and machine learning. You will learn not only the concepts but also the implementation process of those in this book. However, to get to the core of this book, proper knowledge of multivariate calculus and linear algebra is a must. These two are like the prerequisites to understand the intricacy of the language used in the book.

The book of Pattern Recognition and Machine Learning is gaining popularity with readers now. The main reason is the simplicity of understanding and the decent amount of examples for practice. You will find several graphs that perfectly describe distributions of probability. Therefore, learning probably will become more comfortable for you.

What topics does it cover?

  • Probability of inference algorithms
  • Kernel-based models
  • Bayesian Methods
  • Introduction of machine learning and pattern recognition
  • Probability theory basics

Basic facts:

  • Author- Christopher M. Bishop
  • Format- Paperback/ hardcover/ Kindle
  • Publisher- Springer
  • Latest Edition- Second

Natural Language Processing with Python

One of the best machine learning books 2019, Natural Language Processing with Python, is still trending amongst readers. Experts consider this book as the spine of machine learning studies. As expected from the title, the principal language that the author uses to explain the content is Python. As a result, you learn to use NLTK, which is the very famous python program and libraries suite.

The book, amongst everything else, puts light on complex codes of Python that demonstrate NLP. Besides, the precision in the language, along with the analytics, makes it a hit amongst readers. By the time you finish reading this book, you will gain some good knowledge of linguistic structure, unstructured data, and more.

What topics does it cover?

  • Working pattern of human language
  • Trending linguistic databases
  • Integrated techniques from linguistics and artificial intelligence
  • Linguistic structures
  • NLTK (Natural Language Tool Kit)
  • Semantic analysis and parsing

Basic facts:

  • Author- Ewan Klein, Steven Bird, and Edward Loper
  • Format- Available
  • Publisher- O’Reilly Media
  • Latest Edition- First

Bayesian Reasoning and Machine Learning

If you are a beginner and want to focus on machine learning, then this is one of the handbooks that you must own. The book is specially curated for the ones who are highly interested in learning about machines. However, such people do not have any idea about linear Algebra or Calculus.

In the Bayesian Reasoning and Machine Learning book, you will find several exercises and examples. Such a presentation makes it easier for a learner to understand the lessons. Besides, the simplicity of language in this book makes it perfect even for students who are yet to graduate. You will get a few things in association with this book—for example, a software package, online resources, teaching materials, and demo.

What topics does it cover?

  • Approximate interference
  • Probabilistic Reasoning
  • Dynamic Models
  • Algorithms of Naïve Bayes
  • Graphical models framework
  • Probabilistic models

Basic facts:

  • Author- David Barber
  • Format- Available
  • Publisher- Cambridge University Press
  • Latest Edition- Kindle/ Hardcover/ Paperback

Final thoughts

Machine learning is an exciting concept, and with the evolution of automation, it is becoming popular too. Several students are choosing ML as a career, and reading the best machine learning books will help them in their journey.

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