This book covers wide topics in Machine Learning in both theory and practice. It is great for newbies to have a comprehensive understand of Machine Learning (ML). Even for experienced data analyst and Machine Learning engineer, the book provides essential weapons to perform excellent jobs.
After reading, you can expect to answer questions like:
- What is Machine Learning?
- What are the popular algorithms, what problems do they solve, how do they solve, why they are effective?
- What is Neural Network or Deep Learning?
- What are the major topics in ML world?
- What kind of work do Machine Learning professionals do?
I really enjoy this book because:
- the selected topics are useful for readers to learn and do his/her jobs.
- the author can explain core concept in just few words, with the right amount of examples and math
- it is short (only 150 pages)
- it contains references for digging further, and lists topics that are not covered.
- The book contains math not to intimidate reader, but to help reader better understand the algorithms. People taken basic college level math courses (Linear Algebra, Calculus) should have no problems understanding the book.
- The book doesn’t contain hands-on coding materials.
- The book should be treated as a starter book for newbies who just want to quickly pick up enough knowledge and vocabulary. Reader should still seek hands-on experience and in-deep exploring from other places later.
I will end with a short summary of its content:
It first introduces a simple algorithm (Support Vector Machine or SVM) to shed some light on what is Machine Learning.
Then after reviewing of basic math, it starts introducing fundamental algorithms like Linear Regression.
Based on that, it starts to answer questions like how to get the model (gradient descent), best practice like how to data/feature, selection model etc.
After ensuring basic understanding, it introduces more real world problems and corresponding solutions (including Neural Networks and Deep Learning). It again illustrates all related topics for those algorithms.
At last, it shows some advanced topics, other forms of learning, and topics not covered.
Book site: http://themlbook.com/
Amazon link: The Hundred-Page Machine Learning Book