Best Data Science and Machine Learning Books(2019)

October 25, 2019 0 Comment

Data science and machine learning is a fast growing sector and has a bright future. We’re going to discuss some of the best machine learning books.

To address the original question,Which is the best source of learning data science and machine learning?

You will get your answer within the next 15 minutes, if you just stick to the article.

By Jake Vander Plis ” Python Data Science Handbook “

This is one of the most obvious and most accurate books for data science, which I have passed through. It provides a good description of data analysis, processing and visualization using Python.

The book covers a number of libraries such as Ipython, Nummpy, Pandas, Mattplotlib.

What makes it different from others?

IPython, Numpy, and Pandas is very well written, although with built-in examples.

This book contains full details on the proper use of indexing (such as a timestamp as a indicator) and multi-sequencing for the hierarchy structure.

A strong explanation on the complexity of time and how to speed up the code when necessary.

This should be a reference to data science in Python.

Numsense! Data Science for the Layman: No Math Added

As the name suggests, this book is best for beginners or a person with little experience in this field. Every concept is clearly expressed in the book with a clear description.

Each algorithm with a dedicated chapter focuses on how it works with real-world examples.

What makes it different from others?

  1. Real-time example for every algorithm.
  2. Full description of the pros and cons of the algorithm.
  3. Brief summary at the end of each chapter.

Hands-on Machine Learning with Skit-Learn and Tensor Flow: Concepts, Tools, and Technologies to Intelligent Systems…

A brilliant introduction of machine learning for both developers and non-developers. This book does a great job of teaching original ideas, concepts and skills. Also, while its some of the “Hands-on” The parts are old, a large part of the implementation is very clearly explained.

Another highlight of this book is the implementation of each concept. This makes the learning process even simpler. Not only is the theory very well explained, but actually the use of these models makes the understanding of the underlying concepts much more solid and solid.

What makes it different from others?

Every time a new idea or an algorithm is introduced, a code is provided to comply with the reader.

The book covers a wide range of subjects such as linear regression, assembly learning and deep RL.

The chapter on reinforcement learning is intriguing.

All examples are explained with the actual data set.

Data Science By Keller and Brendan Tierney

The book provides a complete overview of the concepts and the logic behind data science. But, it is recommended to have a basic idea about general statistical concepts before going through this book.

What makes it different from others?

Brief introduction to the emerging field of data science, its development, machine learning relations, current uses, data infrastructure issues and ethical challenges.

Good coverage of the ethics of data science.

“Data Science For Dummies” by Lillian Pierson

This book summarizes concepts. An easy to read and understand book. However, a general introduction to diverse topics in data engineering and data science. It’s good for newbie to take a quick look at domains in this area.

What makes it different from others?

The book not only focuses on programming or statistics, but also provides useful introductions for databases, Microsoft Excel, visual design, data storytelling, and a variety of sources for searching for open data.

Here the author uses very simple words and real-life examples to explain complex concepts. It also gives you some resources that will help you acquire the skills you need to become a data scientist.

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