Machine Learning, Just the Essentials, Fast!

The ultimate machine learning and deep learning tutorial that teaches you like a 1:1 private lesson with Hanbit Media's Honkong series, understand with hundreds of hand drawings, and practice right away with just a browser through Google Colab

Wake up your sleeping coding brain. From Python basics to machine learning, all in one book! This book is an introductory computer science book based on MIT lectures, so even beginners who know nothing about programming can easily read it. Wake up your sleeping coding brain with topics essential for introductory programming, such as computational thinking and simple algorithms. Through brain-boosting problems provided in each chapter, we will look at practical topics such as data visualization, simulation, data calculation techniques, and machine learning.

This is a lecture covering LLM theory and practical examples based on <Large Language Models, Just the Essentials!> (Insight, 2025).

This is a course covering the GitHub notebooks and bonus content from <Build a Large Language Model from Scratch> (Gilbut, 2025). GitHub: https://github.com/rickiepark/llm-from-scratch/ <Build a Large Language Model from Scratch> is the Korean translation of the bestseller <Build a Large Language Model (from Scratch)> (Manning, 2024) by Sebastian Raschka. This book provides a way to learn and utilize the operating principles of large language models by building a complete model starting from scratch with OpenAI's GPT-2 model.

This lecture guides readers to deep learning with the most appropriate pace and straight direction, one step from concept to formula and one step from coding. In addition, there are over 100 graphs, illustrations, and diagrams, so you can easily and quickly accept abstract concepts. Another unique feature of this lecture is that you can start practicing right away by simply accessing a web browser without installing a program. After comfortably understanding the theory, you can directly code and conquer four representative deep learning problems with your eyes, so it is not lacking as a textbook for deep learning. The concepts or terms that you must go over are reviewed twice in the ‘Wait! Let’s move on to the next’ corner in the middle of the text and the ‘Memory Card’ corner at the end of the chapter to increase the learning effect. Let’s quickly overcome deep learning head-on with ‘Do it! Introduction to Deep Learning.’

This lecture covers the contents of the Amazon bestseller <Hands-on Machine Learning 2nd Edition>. Using the representative machine learning library, scikit-learn, you will learn various machine learning algorithms and evaluation methods. Also, using the most famous deep learning libraries, TensorFlow and Keras, you will learn theories and practices from artificial neural networks to reinforcement learning. This lecture is not yet complete. One to two lectures will continue to be added every week.

This is a lecture covering LLM theory and practical examples based on <Large Language Models, Just the Essentials!> (Insight, 2025).

This is a newly designed course that allows you to gradually learn the mathematics, theory, PyTorch-based implementation, transfer learning, and GPT's core transformer needed to understand deep learning, based on the instructor's own failed experiences when first learning deep learning.

If you invest 3 hours a day for 6 days, you can learn the basics of deep learning using PyTorch. So, shall we get started?

This is an all-in-one lecture where you can learn the entire process from the basics of deep learning/TensorFlow/computer vision to practical applications through a practical vehicle license plate recognition project. Through various practical exercises, you can develop practical skills to apply the latest deep learning models to custom datasets.

This course teaches you how to perform Object Detection using the latest high-performance deep learning model using the TensorFlow Object Detection API through simple Colab exercises, and learns the principles of the latest deep learning Object Detection models.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

You will build Android/iOS apps based on simple machine learning models and learn the fundamentals of machine learning, model conversion using TFLite, and the process of implementing Android and iOS applications.