I made a promise to myself early summer to read 1-3 new books a month to support my continuous learning on many subjects. Of my recent topics of interests is machine learning as related to different programming. I read Machine Learning in Python: Hands on Machine Learning with Python Tools, Concepts and Techniques by Bob Mather this month and I was not disappointed.

The book offers an excellent overview on what is machine learning, how it relates to programming, and more. I love how Bob Mather explains how machine learning works differently than standard programming as people have practiced it to date. How will we make and carry out machine learning going forward? And how will it differ from our human intelligence process of learning? This book explores these questions and more.

Machine learning is commonly practiced by recursive algorithms, detecting when it is wrong, changing its own values, and attempting to guess correctly. It’s faster than manual efforts of supervised learning but still machine learning has a way to go to teach its self to not only correct errors but also make better decisions going forward.

I now understand all machine learning algorithms focus on parts of a whole to sift accurately and quickly through data. Knowing what data means and how to make the best use of it, is becoming important in every industry and business operation, including noticeable differences in data. I am also delighted to know that Python is really easy to pick up.

The author does an excellent job explaining complex topics by providing as close as possible, real case scenarios. I particularly liked a healthcare working clustering analysis real-world application explained on page 68 of the book. I could envision how machine learning could help in decision-making for more efficient hospital operations, including employee count vs. injuries and supplies. The author shows how to do this real-world cluster analysis without doing complicated math to find the strong correlation.

I recommend those looking to explore machine learning and python keep this book handy because it takes the concept of machine learning and how it can be applied to different types of programming. It is definitely noteworthy for machine learning beginners as well as those continuing their education in machine learning and Python.