Contributors – Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

Contributors

About the author

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.

I am grateful to a number of individuals who helped me build my technical knowledge and bridge the gap between the technical and the business sides of the equation. This list of individuals includes Khaled Fouad Elsayed, Amr Saad Ayad, Beatriz De La Iglesia, Dan Smith, Stephen Cox, Gilad Lotan, Karim Ratib, Peter Tegelaar, Adam Powell, Noel Kippers, and Mark Jager.

About the reviewers

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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