1.Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street

Ace the Data Science Interview, written by two former Facebook employees, is a comprehensive guide to preparing for Data Science, Data Analyst, and Machine Learning interviews. The book contains 201 real interview questions from companies like Facebook, Google, Amazon, Netflix, Two Sigma, and Citadel. It also provides tips on how to break into Data Science, including crafting resumes, portfolio projects, and networking. The book covers topics like Probability, Statistics, Machine Learning, SQL, Coding, Product Analytics, and A/B Testing. It also covers open-ended case study questions and practice with examples from Airbnb, Instagram, and Accenture.

Rated 4.4 on Goodreads.

This book is available for purchase here.

2.Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Deep learning has significantly boosted machine learning, enabling even novice programmers to develop intelligent systems using simple tools. This bestselling book, updated third edition, provides an intuitive understanding of these concepts using Python frameworks like Scikit-Learn, Keras, and TensorFlow. Author Aurélien Géron covers various techniques, from linear regression to deep neural networks, with numerous code examples and exercises. The book covers various models, unsupervised learning techniques, neural net architectures, and uses TensorFlow and Keras for building and training neural nets for various applications, including computer vision, natural language processing, generative models, and deep reinforcement learning.

Rated 4.6 on Goodreads.

You can purchase this book on Amazon.

3.Inside the Machine Learning Interview: 151 Real Questions from FAANG and How to Answer Them

Peng Shao, a former Amazon engineering leader and Twitter Staff ML Engineer, offers a comprehensive guide to mastering Machine Learning interviews. The resource covers the ML interview process, including major sessions on ML Fundamentals, ML Coding, ML System Design, and ML Infrastructure. It provides proven strategies for solving ML problems, step-by-step guidance on tackling ML coding challenges, system design questions, and infrastructure design problems, and a deep dive into interviewers’ mindsets. Shao also provides practical examples and case studies showcasing the history of solutions to ML problems. With over a decade of experience in various industries, Shao’s expertise in machine learning is invaluable.

Rated 4.9 on Amazon.

You can purchase this book here.

4.Data Science and Machine Learning Interview Questions Using Python: A Complete Question Bank to Crack Your Interview (English Edition)

“Data science and machine learning interview questions using Python” is a comprehensive guide for those aspiring to study these fields. The book provides easy-to-remember answers to common interview questions, ensuring a clear understanding of the concepts and terminologies. The book is divided into six chapters, starting with Data Science Basic Questions and Terms, then covering Python Programming, Numpy, Pandas, Scipy, and its applications. It also covers Matplotlib and Statistics with Excel Sheet. The book aims to help readers present their answers in a clear and understandable manner, while also addressing the seriousness and complexity of the subject matter. The book also includes a section dedicated to statistics, as data science is incomplete without mathematics. The book is divided into six chapters, with examples provided for easy reference.

Rated 4.1 on Amazon.

It is available for purchase here.

5.The Hundred-Page Machine Learning Book

“The Hundred-Page Machine Learning Book” is a concise, 100-page manual that covers all major machine learning approaches, from classical linear and logistic regression to modern support vector machines, deep learning, boosting, and random forests. The book is short enough to read in a single sitting and does not assume any high-level mathematical or statistical training or programming experience. It is accessible to almost anyone willing to invest the time to learn about these methods and is recommended for beginners and experienced practitioners. The book illustrates some algorithms using Python code, one of the most popular coding languages for machine learning. It is recommended for both beginners and experienced practitioners seeking to extend their knowledge base in machine learning.

Rated 4.3 on Goodreads.

You can buy this book on Amazon.

6.R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

This book teaches aspiring data scientists how to use R and RStudio to transform data into insights and knowledge. It covers import, transform, and visualize data, as well as communicating results. The book also provides a comprehensive understanding of the data science cycle and the necessary tools for managing details. It includes exercises to practice and is updated for the latest tidyverse features. The book covers creating plots for data exploration, discovering variable types, importing data, learning R tools for problem-solving, and communicating results with Quarto.

Rated 4.6 on Goodreads.

This book is available here.

7.How to Become a Data Scientist: A Guide for Established Professionals

Data science is a highly sought-after career, but the job search process can be challenging for established professionals. This book, written by Dr. Adam Ross Nelson, JD, PhD, aims to help professionals transition into this rewarding field. It covers resume preparation, social media strategies, portfolio creation, interviews, salary negotiation, and navigating early years in the field. The book provides valuable advice on making a transition into or leveling up in data science, including dozens of resources not available elsewhere. It helps overcome feelings of self-doubt, imposter syndrome, and skepticism.

This book is available on Amazon.com.

8.Machine Learning System Design Interview

This book offers a comprehensive strategy for tackling machine learning system design interviews, which are the most challenging technical questions. It provides a step-by-step framework for tackling these questions, including real-world examples and detailed steps. The book is essential for anyone interested in ML system design, whether beginners or experienced engineers. It includes an insider’s take on interviewers’ questions, a 7-step framework for solving any ML system design interview question, 10 real ML interview questions with detailed solutions, and 211 diagrams that visually explain various systems’ functions. The book is designed for those preparing for an ML interview.

This book is rated 4.2 on Goodreads.

You can buy this book here.

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