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Isaac Dec 03, 2020

There are a number of survey books on the market for doing AI with python. I own many of them. Auffarth's approach here is unique in that it takes a do then explain the approach. The chapters are well structured with cookbook examples followed by in-depth explanations. What the author calls 'how to do it' then 'how it works.' The breath covered in the book is also impressive. everything from setting up a basic environment to deploying models into production. Applications range from typical deep learning stuff (like predicting housing prices) to image processing, audio applications, and fraud detection. This has become my latest 'go-to' book when I want to explore a new AI topic. I would recommend it to any data scientist looking to take their skills to the next level.

Amazon Verified review

Eric D. Weber Apr 26, 2021

This book has the right mixture of breadth and depth, which is extremely challenging in the (growing) group of AI literature. What I enjoy the most is that you can both learn a lot of Python and/or learn a lot about ML and Experimentation, depending on your focus. Maybe most important, it allows those newer or intermediate in the field to scale up quickly. For beginners and practitioners, there is a lot of value to be found in the 400+ pages.

Lucinda Linde Feb 19, 2021

"Example isn’t another way to teach, it is the only way to teach." ---Albert EinsteinDisclaimer: This review has been requested by the publisher, and I am giving my honest review of this book. This review is based on reading the book. As with any Cookbook, the proof is in the pudding. I intend to use some of these recipes on my own data sets to see if the techniques help me get better results.OverviewArtificial Intelligence with Python Cookbook by Ben Auffarth, is a jam-packed masterclass in applying AI to a selection of important business problems. The book will be helpful to someone who had made many models, and wants to up their game to using more sophisticated AI.What I love about this book:The examples are very relevant. There are over 20 examples which range from detecting anomalies as in Fraud Detection to Time Series to Customer Lifetime Value to modelling spread of disease and many more. These are all applications that have proven business or social ROI.Chapter 1 is filled with helpful tips to set up Google Colab and Jupyter Notebook, as well as ways to write better code. These include speeding up and parallelizing code, putting in progress bars, auto-reloading packages and more. There are also really useful visualization techniques such as correlation heat maps and pair plot. These are fast ways to get a bird's-eye view of exploratory data analysis (EDA).Each chapter is its own guidebook to using a particular technique. For example, I recently did a project on Time Series and got ok results. Chapter 2 gives me additional ways to utilize SARIMAX, which I will try out. There are also scripts using FBProphet, which may be a better way to address Time Series. The code examples are in the book, but also on GitHub for easy copy-and-pasting. The book explains the rationale behind the code.The examples used from chapter to chapter build on each other. The examples start with the basic, such as the iris classification problem, and model using a variety of neural networks. There are chapters on NLP, machine vision, audio files and even deploying AI in production.The visualizations are very helpful to understanding the insights that are being generated from the code. Having the code to make those visualizations is extremely helpful.I will use this book as a reference and starting point for trying out techniques I haven't before. Can't wait to try out the examples in the book.What I don't like about the bookFor topics that I am really unprepared to absorb, the book gives links to material that can cover the basics. It makes me wonder if this book could perhaps be the springboard to multiple books each of which takes a few techniques, and brings the reader up to speed more gently, with examples of increasing complexity so the core concepts can be learned.Overall, "Artificial Intelligence with Python Cookbook" is very well thought out and a fantastic one place to start upping one's game in applying AI to a variety of valuable applications.

Andreas Mueller Dec 09, 2020

I've been impressed by the wide overview of the book, which really spans the gamut of what AI means, from classification to search algorithms and A/B testing. The book focuses on some standard tools but also branches out to surface some lesser known libraries that can come in handy.While 468 pages can only give a taste of each topic, the book is jam-packed with examples and serves as a good starting point with plenty of references.

Murphy Choy Aug 26, 2021

The book is a great recipe book for folks who want quick results without going through reams of content. I love the easy to read structure of this book.

Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6

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