The best Books to Learn Machine Learning in 2024

Hello dear reader! Hope you are doing well. In this post, we will compare what for us are three of the best books to learn Machine Learning in 2021. It’s a hard task, but in the end, we will expose the one that we would pick if only a single one could be chosen. Let’s go! 

These are the three books that we would choose as the best books to learn Machine Learning in 2021, taking into account both, their theoretical rigor and practical guidance towards the implementation of the explained theoretical concepts. 

• Python Machine Learning by Sebastian Rashcka
• Hands on Machine Learning with Scikit-Learn, Keras & Tensorflow
• Building Machine Learning Powered Applications

You can find reviews of all of them on the previous links or within our Machine Learning books category. Let’s start with Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 

• Python Machine Learning: Killing it with code

Python Machine Learning is one of the best books for learning how to implement Machine Learning algorithms. It does a great job introducing the theory and main concepts behind the most known Machine Learning algorithms, and the standard Data Science pipeline. 

However, its main strength, and what makes the book a great companion in the learning career of any Machine Learning enthusiast, is the great practical implementations and detailed code explanations it includes. 

We think it is one of the best Machine Learning books out there as it presents an efficient and professional way of coding in Python with detailed code explanations that are not seen very much elsewhere. 

We’ve said a lot of times before, Machine Learning is as much of knowing the main statistical concepts behind each algorithm and knowing how to best and most efficiently implementing it.  

• Hands-on Machine Learning with Scikit-Learn, Keras & Tensorflow

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems is thought for beginners in Machine Learning, that are looking for a practical approach to learning by building projects and studying the different Machine Learning algorithms within a specific context. 

Whereas to make the most out of Python Machine Learning we recommend already having some Machine Learning experience (as it is mainly focused on code), Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow has more bottom-up explanations of the main ML concepts, thought for beginners, while also containing very nicely explained code snippets and implementations. For those looking to deepen their understanding of AI and machine learning, exploring AI and Machine Learning courses can provide a solid foundation, from theory to practice, helping you build the expertise needed to tackle complex machine learning challenges with confidence.

After completing the whole book you should be ready to face a project by yourself and be comfortable with the different steps in this process. You will be able to code most if not all of the machine learning algorithms in Python, and understand what you are doing through the whole process. 

• Building Machine Learning Powered Applications

This is a book that you won’t probably find in many Best Machine Learning books lists, however, we love it. 

The goal of Building Machine Learning Powered Applications: Going from Idea to Product is to explain in detail how to exploit Machine Learning models to create beautiful, efficient, and useful applications and products that can provide real value. 

Again, like Python Machine learning it is a book that is very oriented towards getting Machine Learning modes out into the world and providing real value, but even more so.

It really focuses on how to turn a working Machine Learning model into a product or service that is used: the best practices around it, possible problems, and how to monitor these models in order to guarantee that they keep providing good results. 

While there are piles of Machine Learning books out there that detail how ML algorithms work and how to implement them, this is one of the few books we have come across that really dives into how to successfully carry out Machine Learning projects from end to end.  

Conclusion 

We have to admit that at the start we lied

There is no clear winner amongst these three, rather, we would pick one over another depending on our level of expertise in Machine Learning, following this criterion: 

• If you are beginning with Machine Learning and programming in Python, take Hands-On Machine Learning with Scikit-Learn & TensorFlow. 
• Otherwise, if you’ve already got a good grasp of the theory and core concepts behind Machine Learning algorithms, take Python Machine Learning. 
• Finally, if you are a coding expert that knows all the hyper-parameters, best ways to plot evaluation metrics, and how to best implement and use the models, and you want to best itegrate them in applications and deploy them, take Building Machine Learning Powered Applications. 

Easy right? It all depends on your level of expertise. 

As you can see we’ve left out from this list other top books that we think are also fantastic like The Elements of Statistical Learning, as we’ve wanted to focus on books that contain coding in Python.

We will, however, make more lists like this one in the future. 

That is all, we hope you’ve enjoyed this list with The Best books to learn Machine Learning in 2021! Don’t forget to follow us on Twitter and stay tuned!