Although I majored in mechanical engineering, I have worked continuously reading and writing code since graduating. I am a Google AI/Cloud GDE. I run the TensorFlow blog (tensorflow.blog) and explore the fascinating boundary between software and science by writing and translating books about machine learning and deep learning.
I have written 『Deep Learning You Study by Building It Yourself』 (Hanbit Media, 2025), 『Self-Study Machine Learning + Deep Learning (Revised Edition)』 (Hanbit Media, 2025), 『Self-Study Data Analysis with Python』 (Hanbit Media, 2023), 『The Art of Conversing with ChatGPT』 (Hanbit Media, 2023), and 『Do it! Introduction to Deep Learning』 (Aegis Publishing, 2019).
『Hands-On Machine Learning with Scikit-Learn and PyTorch』(Hanbit Media, 2026), 『Deep Learning from the Keras Creator (3rd Edition)』(Gilbut, 2026),『LLM Fine-Tuning, Fast and Focused!』(Insight, 2026), 『LLM & AI with PyTorch』(Hanbit Media, 2026), 『Large Language Models, Fast and Focused!』(Insight, 2025), 『Machine Learning, Fast and Focused!』(Insight, 2025), 『Learn LLMs by Building from Scratch』(Gilbut, 2025), 『Hands-On LLM』(Hanbit Media, 2025), 『Machine Learning Q & AI』(Gilbut, 2025), 『Mathematics for Developers』(Hanbit Media, 2024), 『Practical ML Problem Solving with Python』(Hanbit Media, 2024), 『Machine Learning Textbook: PyTorch Edition』(Gilbut, 2023), 『Stephen Wolfram's ChatGPT Course』(Hanbit Media, 2023), 『Hands-On Machine Learning, 3rd Edition』(Hanbit Media, 2023), 『Generative Deep Learning by Building, 2nd Edition』(Hanbit Media, 2023), 『Python That Awakens Your Coding Brain』(Hanbit Media, 2023), 『Natural Language Processing with Transformers』(Hanbit Media, 2022), 『Deep Learning from the Keras Creator, 2nd Edition』(Gilbut, 2022), 『Machine Learning&Deep Learning for Developers』(Hanbit Media, 2022), 『Gradient Boosting with XGBoost and Scikit-Learn』(Hanbit Media, 2022), 『Learn Deep Learning with the Google Brain Team with TensorFlow.js』(Gilbut, 2022), and 『Machine Learning with Python Machine Learning Libraries (2nd Revised Edition)』(Hanbit Media, 2022), among several dozen books translated into Korean.
Weekly Reading Amount: Read 2 chapters per week, from Chapter 1 to Chapter 10
A certificate of completion will be issued if you complete all missions and reach a 100% course completion rate within the challenge period. If the missions are not completed, a certificate cannot be issued.
E-book release date: November 14th; e-books are also included in the book purchase verification.
Challenge Benefits
Free coupon for the <Machine Learning, Just the Essentials!> course (valued at 40,000 KRW)
Additional benefits upon completion of the challenge
Free coupon for the <Large Language Models, Fast & Focused!> course (valued at 40,000 KRW) (Scheduled to be issued after December 17th)
<LLM Fine-tuning, Just the Essentials Fast!> (To be published) Challenge free coupon provided (20,000 KRW value) (Challenge opening and free coupon issuance schedule to be announced later)
Benefits are provided as Inflearn coupons. Please be sure to refer to the precautions below.
<Precautions> Must Read!!!
On November 13th, the free coupon issuance link for the <Machine Learning, Fast & Core!> course and the 50% discount coupon issuance link for the <Learning LLM by Building from Scratch> course will be sent to challenge participants via the email registered on Inflearn.
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Upon completion of the challenge, a free coupon link for the "Large Language Models, Fast & Core!" course will be sent to your email immediately. Additionally, a free coupon link will be emailed to you when the challenge for the upcoming book "LLM Fine-Tuning, Fast & Core!" (to be published) opens. The methods for issuing and using the coupons are the same as described above.
About the Book
Less complex theory, more essential core concepts!
The most concise guide to mastering machine learning
This book is a bestseller translated into 11 languages and used as a textbook in thousands of universities worldwide, explaining machine learning in a concise and clear manner. It covers everything from basic mathematical concepts to core algorithms, deep learning, and neural networks step-by-step, providing a complete toolkit for solving modern machine learning problems such as clustering, topic modeling, metric learning, and recommendation systems. By connecting theory with practical implementation focusing on essential industry skills, it allows anyone to learn quickly and effectively.
Based on extensive practical experience, the author explains techniques that can be directly applied to real-world projects, such as feature engineering, regularization, handling imbalanced datasets, ensembles, and model evaluation. Composed of intuitive explanations and examples rather than being bogged down by complex formulas, it is useful for everyone from beginners who want to build a solid foundation to practitioners looking to expand their professional capabilities.
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Reviews
“It is remarkable to have covered such a wide range of topics in just about 100 pages. While most thin books omit the mathematics, this book does not. I also truly loved how it clearly explains core concepts in short sentences. This book is useful for beginners and also helpful for experienced practitioners who need a broad overview.” - Aurélien Géron (Senior AI Engineer, Author of 《Hands-On Machine Learning》)
“Andriy attempted the impossible task of summarizing the entirety of machine learning in about 100 pages, and he has done an excellent job of selecting both theoretical and practical content useful for practitioners. This book will provide a great foundation as an introductory guide for readers new to machine learning.” - Peter Norvig (Research Director at Google, co-author of the world-renowned AI textbook AIMA)