8 Must-Read Machine Learning Books: Introductory, Intermediate, Expert Level

COVID – 19 has left drastic effects in the modern world. Therefore, machine learning has proven to be a game-changer for the industries as a part of innovation.

Once it used to be considered a luxury, but now businesses recognize it as a necessity for survival.

People who used to see advanced technology as a threat are now seeing it as an ally.

The year 2020 has brought up Machine Learning as an invaluable asset to have for organizations across the globe.

All of these trends show how important it is to learn machine learning. The best ways are to get a certified course. Madrid software trainings in Delhi is one of the best Institute for Machine learning in Delhi that provides machine learning classes in Delhi (for you if you are a Delhiite).

Apart from a course or even along it’s good to read the most trending and popular books on machine learning.

In this article, we are giving you a list of books and a brief description of them. I recommend you to read them if you have generated a strong desire to learn machine learning.

I have saved your time and efforts to find a bunch of insanely awesome books on machine learning.

Enjoy!

1.    Hand on Machine Learning Sickit – Learn, Keras and TensorFlow

This book is written by Aurelien Geron. With self-containing chapters, you could either read from cover to cover or jump to the chapters most useful for you.

It is great for a broad knowledge range, If you have grain in knowledge about machine learning, this book would be a good starting point, or if you are looking to learn how to customize layers in neural networks, this book talks about how to do that with subclassing Keras layers.

2.    Automate the Boring Stuff with Python

In this book, Albert Sweigart represents and explains the important concepts with such simplicity that it gives good knowledge to a beginner and takes you to an intermediate level.

It offers easy to understand algorithm/logic and some mini projects too!

3.    An introduction to Statistical Learning with Application in R

I can’t praise this book enough! It is the best book for an introduction to machine learning theory. It has the brains of 4 authors.

It profoundly covers concepts of Linear Regression, mathematically covers cross-validation, Logistic Regression, PCA, Random Forest and trees, and clustering.

On top of that, it covers the relationship between SVM and logistical regression - history of hype behind kernel methods in SVM.

4.    Python Machine Learning

If you’ve completed a bunch of machine learning projects for you already and want to get accustomed to working with machine learning models, this book and its second edition are what I would recommend you.

The book is very comprehensive, up-to-date, and keeps a nice balance of intuition and mathematical rigor.

Rather than using higher-level machine learning libraries like Scikit, TensorFlow, and Keras, the author walks through the algorithms in Python and Numpy. Overall, this book has the exact balance between being hands-on with the code and explaining the complex math.

5.    Deep Learning

This book covers everything that is needed from the ground up to be well versed in Deep Learning. All the background mathematics required and Machine Learning subject as a whole is covered to the point, focusing on what is needed as of present.

Every line in the book develops over the previous text systematically so that no extra information is provided. The sections are well categorized.

As the field of deep learning is quite big, the authors have done a great job in providing the most essential material for designing and implementing the deep learning algorithms.

6.    Python Crash Course

This book is best for a person having some basic knowledge in python so that you can jump into intermediate or some equivalent. The Projects are quite amazing, every concept is explained concerning the project.

The first half of the book teaches you basic programming concepts, such as variables, lists, classes, and loops, and practice writing clean code with exercises for each topic. It allows you to learn how to make your programs interactive and test your code safely before adding it to a project. In the second half, you'll get to put your new knowledge into practice with three substantial projects provided in this book.

7.    Deep Learning with Python

To read this amazing book, you need intermediate Python skills.

Deep Learning with Python introduces you to the field of deep learning using the Python language and the powerful Keras library. Well written by popular Keras creator and Google AI researcher Fran?ois Chollet, this book will build your understanding through intuitive explanations and practical examples.

The language used is simple to understand. The topics are presented in a well-organized manner and It is captivating to read with a computer by your side to practice the programming.

8.    Advances in Financial Machine Learning

You will learn how to structure Big data in a way that is open to ML algorithms, how to research with ML algorithms on that data, how to utilize supercomputing methods, and how to backtest your discoveries while eliminating false positives. The book throws light on the real-life problems faced by practitioners daily and provides scientifically sound solutions using math that is supported by code and examples. 

These books in this article are the best sellers of all the time and I have mentioned which ones are for beginners and experts in the description.

If you’re looking to join a machine learning classes in Delhi, Madrid Software Trainings is ready to help you kick-start your ML career.