Image of the book cover.

The book that helped me become a better Machine Learning Engineer

Now before we get started I would like to let all of you know that I have never been a huge fan of books when it comes to learning anything related to tech. I am a firm believer of project based learning and still am, but through the read through of this book I came to the realization that how much I am missing by not reading. The many tidbits this book gave me helped me strengthen my fundamental knowledge more so than what the 3–4 intermediate projects I had done did.

But why am I telling you this?

Because if you are a project based learner like me take some time off and just peruse through this book.

Believe me you won’t regret it. I will tell you why?

The book I am talking about is Hands on Machine Learning with TensorFlow and Scikit-Learn by Aurelien Geron.

So what makes this book so special? Let’s get into it.

General Review:

Unlike the many other books in this genre, Hands on Machine Learning with TensorFlow and Scikit-Learn takes a more pragmatic approach when it comes to introduce machine learning and deep learning topics. It goes down the machine learning rabbit hole but not so deep that a newcomer might be put off. Just enough to keep you hooked while also providing value.

It is suited for someone who is just starting out or is in an intermediate position in his machine learning journey.

It goes all the way from explaining the current machine learning landscape to talking about SVM’s and Random Forests. It also covers a lot of abstract deep learning concepts like Computer Vision and Natural Language Processing. And the cherry on the top is that it will teach you how to host your model.

The approach this book takes that makes it stand out from the others is that it teaches you machine learning concepts from an application based point of view rather than a research based point of view. So people who are interested in Applied Machine Learning will be at home with this book.

For each topic that it covers there is a code snippet either in Scikit-Learn or TensorFlow which shows you how to implement it. In my opinion this is very useful as it helps you to build an application based understanding of the topic at hand. You do learn the theoretical aspect of things but along side that you also learn to apply it to real world scenarios.

The cherry on top is that at the end of each chapter there are a bunch of exercises you can do to test whether you have understood the topic clearly or not.

Final Thoughts:

In my opinion this book is best suited for people who are interested in applied machine learning and the coding aspect of things. The book does go deep in the machine learning rabbit hole but not enough to build research based fundamentals.

Even then this is a nice read for a beginner regardless of what you are looking for in the machine learning space.

So if you are deep into the research side of things this book isn’t for you. But if you are just starting out or want to learn about some machine learning best practices then this book is for you.




Aspiring data scientist and ML enthusiast

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Burhanuddin Rangwala

Burhanuddin Rangwala

Aspiring data scientist and ML enthusiast

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