✓ Thoughtful Machine Learning: A Test-Driven Approach (English Edition) eBook: Matthew Kirk: Amazon.fr: Amazon Media EUS.à r.l. ¹ Download by É Matthew Kirk
✓ Thoughtful Machine Learning: A Test-Driven Approach (English Edition) eBook: Matthew Kirk: Amazon.fr: Amazon Media EUS.à r.l. ¹ Download by É Matthew Kirk Totally disappointed Didn t get much of anything from this book A lot of space wasted on Ruby code that s not necessarily easier to follow than Python Each chapter just presents key formula and doesn t explain underlying concepts well even at the basic level Machine Learning in Action does a much better job at practical introduction to machine learning And where s the TDD stuff Very misleading title I thought things like cross validation are already an integral part of machine learning.
Learn How To Apply Test Driven Development TDD To Machine Learning Algorithmsand Catch Mistakes That Could Sink Your Analysis In This Practical Guide, Author Matthew Kirk Takes You Through The Principles Of TDD And Machine Learning, And Shows You How To Apply TDD To Several Machine Learning Algorithms, Including Naive Bayesian Classifiers And Neural NetworksMachine Learning Algorithms Often Have Tests Baked In, But They Cant Account For Human Errors In Coding Rather Than Blindly Rely On Machine Learning Results As Many Researchers Have, You Can Mitigate The Risk Of Errors With TDD And Write Clean, Stable Machine Learning Code If Youre Familiar With Ruby , Youre Ready To StartApply TDD To Write And Run Tests Before You Start CodingLearn The Best Uses And Tradeoffs Of Eight Machine Learning AlgorithmsUse Real World Examples To Test Each Algorithm Through Engaging, Hands On ExercisesUnderstand The Similarities Between TDD And The Scientific Method For Validating SolutionsBe Aware Of The Risks Of Machine Learning, Such As Underfitting And Overfitting DataExplore Techniques For Improving Your Machine Learning Models Or Data Extraction A little off center in a crowded field, and therefore worthy.
I recommend The content of the book is interesting for people who already know ML and are interested in a practical approach to it.
The 1 star rating is due to the amount of mistakes in the book Between numbers which don t add up, graphs that don t fit the legend, typos in equations, I recommend reading the errata first and annotating the pages with errors, otherwise, it s confusing.