Writing an all-encompassing book on Python machine learning is difficult, given how expansive the field is. But reviewing one is not an easy feat either, especially when it's a highly acclaimed title such as Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd Edition. The book is a best-seller on Amazon, and the author, Aurélien Géron, is arguably one of the most talented writers on Python machine learning. And after reading Hands-on Machine Learning, I must say that Geron does not disappoint, and the second edition is an excellent resource for Python machine learning. Geron has managed to cover more topics than you'll find in most other general books on Python machine learning, including a comprehensive section on deep learning.

Artificial intelligence (AI) is no longer theoretical. It's growing in use everywhere from determining the best spot on the court to shoot a three-pointer to assisting doctors with finding a cure for cancer. Judging by advertisements and creative campaigns (AI can make you a taco!), everyone is using it. Despite this prevalence of AI marketing, the truth is quite different. A lot of AI marketing is just buzz.

AI and ML are starting to transform business. Gartner estimates that AI technology will generate $3.9 trillion in business value by 2022. Soon, companies that do not invest in AI risk falling behind competitively. But having said that, organizations should not adopt AI just to stay on trend. It's crucial to build an AI business case, assess the company's AI readiness and create the program in the right way.