A Practical Guide to Artificial Intelligence and Data Analytics

· Rayan Wali
5.0
2 reviews
Ebook
600
Pages
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About this ebook

Whether you are looking to prepare for AI/ML/Data Science job interviews or you are a beginner in the field of Data Science and AI, this book is designed for engineers and AI enthusiasts like you at all skill levels. Taking a different approach from a traditional textbook style of instruction, A Practical Guide to AI and Data Analytics touches on all of the fundamental topics you will need to understand deeper into machine learning and artificial intelligence research, literature, and practical applications with its four parts:


Part I: Concept Instruction

Part II: 8 Full-Length Case Studies

Part III: 50+ Mixed Exercises

Part IV: A Full-Length Assessment


With an illustrative approach to instruction, worked examples, and case studies, this easy-to-understand book simplifies many of the AI and Data Analytics key concepts, leading to an improvement of AI/ML system design skills.

Ratings and reviews

5.0
2 reviews
Malik
August 24, 2021
Love it, great book for data scientists at every level. Rayan gives a good intro to Deep Learning as well as covers basics of data structures, Intro to SQL and Python. He He also covers the applications of AI and real world examples with case studies. I highly recommend this book.
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Zain Sheikh
August 23, 2021
Great book. Goes in a lot of details.
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About the author

Rayan S. Wali is a Software Engineer at Amazon Inc. He holds a Bachelor’s Degree of Computer Science and Applied Economics from Cornell University. At his time at Cornell (2019 - 2022), he served as an Educational Facilitator for Data Structures and Multivariable Calculus and a Teaching Assistant for Operating Systems. He has interest in Artificial Intelligence research and developing Machine Learning systems, and is constantly working on new projects and designing novel algorithms with the hope to improve current AI technologies.


​He launched A Practical Guide to AI and Data Analytics in 2021, with the goal of improving the data science education. The ten practical case studies and the full-length assessment at the end of the book are designed to further strengthen data science and analytics concepts by giving diverse approaches to problems.

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