Cracking The Machine Learning Interview

by Nitin Suri

"A breakthrough in machine learning would be worth ten Microsofts." -Bill Gates Despite being one of the hottest disciplines in the Tech industry right now, Artificial Intelligence and Machine Learning remain a little elusive to most.The erratic availability of resources online makes it extremely challenging for us to delve deeper into these fields. Especially when gearing up for job interviews, most of us are at a loss due to the unavailability of a complete and uncondensed source of learning. Cracking the Machine Learning Interview Equips you with 225 of the best Machine Learning problems along with their solutions. Requires only a basic knowledge of fundamental mathematical and statistical concepts. Assists in learning the intricacies underlying Machine Learning concepts and algorithms suited to specific problems. Uniquely provides a manifold understanding of both statistical foundations and applied programming models for solving problems. Discusses key points and concrete tips for approaching real life system design problems and imparts the ability to apply them to your day to day work. This book covers all the major topics within Machine Learning which are frequently asked in the Interviews. These Supervised and Unsupervised Learning Classification and Regression Decision Trees Ensembles K-Nearest Neighbors Logistic Regression Support Vector Machines Neural Networks Regularization Clustering Dimensionality Reduction Feature Extraction Feature Engineering Model Evaluation Natural Language Processing Real life system design problems Mathematics and Statistics behind the Machine Learning Algorithms Various distributions and statistical tests This book can be used by students and professionals alike. It has been drafted in a way to benefit both, novices as well as individuals with substantial experience in Machine Learning. Following Cracking The Machine Learning Interview diligently would equip you to face any Machine Learning Interview. We have also provided Python code snippets for some of the questions using Scikit-Learn. You can find them on github as

166 pages, Kindle Edition

Published December 15, 2018

Reviews

[Paul · Rating 2 out of 5]

This book reads like a raw compilation of blog posts. Some contents are duplicated over chapters. Some paragraphs are contradictory to another (eg., when they explain the "linear" in linear regression.) No references/citation for verification or further reading.

[Lucille Nguyen · Rating 3 out of 5]

Readable review of ML concepts. Not terribly focused around interviews, but still, good amount of things to review if you're going into a ML interview. More "overview" than a true "cracking... interview" but good for what it is, even if the title is misapplied.

[Heemanshu Suri · Rating 5 out of 5]

A must-have book for anyone who is interested in learning Machine Learning concepts from scratch. The best part about the book is that it covers all the major ML concepts from mathematical, statistical and algorithm point of view, and demonstrates how to deploy them in the real-life applications.