After a month of living life, it’s time for another book review. This month, let’s review the most sought-after book from the Machine learning world “Hands-on Machine Learning with Scikit-Learn, Keras and Tensorflow” by Aurélien Géron (2nd Edition Updated for Tensorflow 2)
First, let me discuss what is in the book, and later give my honest review of what I think about the book.
The book is meant to provide concepts, tools, and techniques to build intelligent systems.
The book assumes that the readers have some python programming experience and are familiar with libraries like numpy pandas, and matplotlib. In addition to that, having reasonable math knowledge will help.
The book suggests the readers try out the examples provided online and approach the concepts with hands-on coding.
The book is divided into 2 parts:
Part 1: “Fundamentals of Machine Learning” with topics such as:
What is Machine Learning
Steps in an ML project
Model fitting
Optimize cost function
Data cleaning and preparing
Feature engineering
Tuning hyperparameters
Challenges of ML
Supervised and Unsupervised algorithms
Dimensionality reduction
Part 2: “Neural Networks and Deep Learning” includes topics such as:
What are Neural Networks
Building and training Neural Networks using Tensorflow and Keras
Most important architectures
Techniques in training deep neural networks
Loading and preprocessing large amounts of data efficiently
Training and deploying TensorFlow models at scale.
There are plenty of code examples in this book with sample outputs displayed.
My Review:
- This book is enormous in terms of the number of pages and content provided. It may take months or even years to finish this book and code every problem.
- The part 1 section of the book is vital to most readers since it covers most of the widely used algorithms and techniques.
- The part 2 section is an advanced section with most of the content on Deep Learning and Neural Networks architecture. This section is useful for readers who already have wide experience in those fields.
- The good part is the author has provided a detailed curated checklist on what needs to be followed in an ML project.
- Every chapter of this book has an exercise section with questions and solutions most of which are asked during interviews based on my experience)
- Never miss the Appendix section of the book as that contains all the solutions for the questions under the exercise section.
Overall, this book is a complete one-stop destination for any ML readers to build projects and get hands-on experience. The book can be used in several ways:
- Expanding your hands-on knowledge
- Build your portfolio
- Pick one algorithm and thoroughly build and deploy a system
I personally feel this was one of my great investments as I will need this from time to time. My recommendation is 5/5. Hope you all enjoyed this review time. Until next week, Happy Reading!!
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