Tarek Amr - Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python

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Tarek Amr has 8 years of experience in data science and machine learning. After finishing his postgraduate degree at the University of East Anglia, he worked in a number of start-ups and scale-up companies in Egypt and the Netherlands. This is his second data-related book. His previous book covered data visualization using D3.js. He enjoys giving talks and writing about different computer science and business concepts and explaining them to a wider audience. He can be reached on Twitter at @gr33ndata. He is happy to respond to all questions related to this book. Feel free to get in touch with him if any parts of the book need clarification or if you would like to discuss any of the concepts here in more detail.

Jamshaid Sohail is passionate about data science, machine learning, computer vision, and natural language processing and has over 2 years of experience in the industry. Currently, he is working as a data scientist at Systems Limited. He previously worked at a Silicon Valley-based start-up named FunnelBeam as a data scientist, working with the founders of the company from Stanford University. He has completed over 66 online courses on different platforms. He is an author of the course Data Wrangling with Python 3.X from Packt and has reviewed multiple books and courses. He is also developing a comprehensive course on data science at Educative and is in the process of writing books at multiple firms.

Prayson Wilfred Daniel bends Python, Bash, SQL, Cypher, JavaScript, Scala, Git, Docker, MLflow, and Airflow to make raw data tell their past, present, and future stories. Building business-driven innovative solutions with a strong focus on microservices architectures and taking into consideration DevOps is what he is passionate about. Prayson holds an MSc. in Information Technology and Persuasive Design from Aalborg University and seeks to help companies gain a competitive advantage from artificial intelligence, particularly machine learning.

Eugene Y. Chen is a machine learning engineer/researcher who wants to make the world a better place with smart software. When he is not building software, he enjoys thinking about and researching machine learning. He has published many peer-reviewed academic works, most recently at the KDD Workshop on Mining and Learning from Time Series on the topic of ensemble learning. He is a contributor to the scikit-learn project.

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You have already seen Harvard Business Review describing data science as the sexiest job of the 21st century. You have been watching terms such as machine learning and artificial intelligence pop up around you in the news all the time. You aspire to join this league of machine learning data scientists soon. Or maybe, you are already in the field but want to take your career to the next level. You want to learn more about the underlying statistical and mathematical theory, and apply this new knowledge using the most commonly used tool among practitioners, scikit-learn.

This book is here for you. It begins with an explanation of machine learning concepts and fundamentals and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms and shows you how to use them to solve real-life problems. You'll also learn various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you'll gain a thorough understanding of its theory and learn when to apply it to real-life problems.