Machine Learning is evidently a vast field and its study is one of the most enlightening tasks one could ever undertake. Today most of the business operations and innovations are done around ML and its innovative applications. A number of professionals are up-skilling themselves with advanced ML knowledge to thrive ahead in their respective fields. They are more keen on learning the offerings, advancements, experts' opinion and various nuances in context to machine learning or artificial intelligence (AI) as a whole. If you are tech-enthusiast and looking forward to learning some new ideas and innovations about machine learning, you can find plenty of comprehensive books that demonstrate and offer various skills, advice and learning opportunities.

This book is probably best for those of you who have read the theory, but are not quite sure how to turn that theory into something useful. Or for those who simply hunger for a survey of how machine learning can be applied to the web, and need a non-mathematical introduction. My area of strength happens to be neural networks (my MS thesis topic was in the subject), so I will focus on that. In a few pages of the book, the author describes how the most popular of all neural networks, backpropagation, can be used to map a set of search terms to a URL. One might do this, for example, to try and find the page best matching the search terms.