Luis worked as a postdoctoral researcher at the Laboratoire de Combinatoire et d’Informatique Mathématique at the University of Quebec at Montreal.
Today Luis is a research scientist in quantum artificial intelligence at Zapata Computing. He previously worked as a Machine Learning Engineer at Google, Lead Artificial Intelligence Educator at Apple, and Head of Content in Artificial Intelligence and Data Science at Udacity.
The opening chapters provide a good overview to Machine Learning before addressing linear regression. As with previous Grokking series books (below) readers will certainly be learning about supervised algorithms that classifying data.
However Luis is also addressing a much needed understanding of established methods to simplify data clean up in order to make reporting actionable. Likewise, an understanding the basics of Python will make this book’s examples easily understood. To his credit, Luis’ feels that anyone with high school algebra will be able to understand his book.
Some ML deep dives are very revealing
Again, within chapter 12, Combining models to maximize results: Ensemble learning is one lessons that sticks. In fact, Luis outlines the machine learning models, yet is focusing on learner with his code available via GitHub. In addition Luis is providing a good explanation of the ‘weak learner’ 1-5 technique. So, here it may begin to add a layer of deeper understanding for decision trees and random forests.
However this is perhaps the types of insight that will trigger readers to learn more about enhancing Machine Learning datasets. Luis is addressing XGBoost and is providing a rich set of online resources. Finally, it should be shared that Luis hosts a popular AI channel that readers should explore to gain even further insights to AI and ML.
In conclusion, Grokking Machine Learning is a much recommended book for anyone interested in further understand Machine Learning, a cornerstone of AI. The Grokking series does not disappoint and many will benefit.