Machine Learning Simplified: A Gentle Introduction to Supervised Learning, Andrew Wolf, 2022
In this world of increasing influence of artificial intelligence (AI), teaching computers how to learn is increasingly important. This book attempts a gentle introduction to the subject
The first thing to consider is the quality of your data. Is it in the right standard of measurement (for instance, centimeters vs. inches)? Are there any outliers or other extraneous bits of data that can be deleted? Once the data is "squeaky clean," what sort of model will the data go into? Will usable data come out the other end? After the data comes out, evaluate it and make sure that it is usable. If the answer is No, consider changing the model, and repeat.
This is where calculus starts to rear its ugly head. The author then gets into gradient descent algorithms, basis expansion, choosing regularization strength, bias-variance decomposition and leave-one-out cross validation.
This book works really well as an introduction to machine learning. It also works as a refresher course for those who already in the field. Yes, it's worth reading, even if you take it just one chapter at a time.
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