I wonder if one day we all start using algorithms for any decision we make in life. We can just have a simplified way to throw all variables in the decision-making mix and assign a task to a machine to perform it for us. At least then we will have machines to blame if the results of a decision don’t turn out as we expect them to! Jokes aside, this book is a pretty simple explanation of machine learning intended for beginners, which you will be able to use not only as a Python developer, but also to understand how trees and forests are used as a metaphor for the logical processes of machine learning.

The example for Google driverless vehicles is used to explain how algorithms are used to help the machine analyze the data and come to logical outcomes. If you’ve never really touched upon computer algorithms, you may need to spend some more time in finding the parallel between machine and real learning. However, decision trees and random forests are just complex logical processes - sometimes our brains get into such analysis without even being aware of it. As inconvenient as it may seem, machines are better equipped to solve many problems. The book also goes into the common problems of decision trees and random forests, including practical training examples and visual presentation. It may now be an area for techies, but I think that in a few years, ML will be in the mainstream general knowledge. So. it’s good to get prepared!