At first, you might find the title of the book technical, but actually, the book is for broader consumption by people who have basic knowledge of computers as well. From a young student of high school to a mature adult, this book is extremely helpful for having a career in machine learning.
I was keen to read this book because of my interest in EdTech products and to guide youngsters or college students about this. For getting the right tool, you need the help of artificial intelligence, and for that machine learning is important to be known.
The author stated the five basic approaches to machine learning to act as the foundation entity. They are:
Symbolists – Logical
Connectionists – Neural Networks
Evolutionaries – Genetic Programs
Bayesians – Graphical Modeling
Analogies – Specific Instances
Each of these 5 approaches is essential for building the master algorithm. And with the correct use of these algorithms, machine learning will prosper and we could see its true outcomes. The future of machine learning is quite bright, it’s just that the users need to crack down or decode the right applications.
It is a great read because it gives you a summary of the introduction to the cognitivism school of sciences. The book also provides a concise note on solving Bayes, giving a concise description of how it came into existence, and explaining how it is practically at work, things around us like- neurons, self-driving cars, electrons, etc.
The book is full of several interesting incidents. I really enjoyed the few last chapters that were essentially based on the discussion about the future of AI, privacy, digital values, and the author’s point of view on Kurzweil’s looming ‘singularity’
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