This is a basic R programming course for beginners. Just follow along with me and type in the code twice. You will naturally become a beginner!

This is a lecture for non-majors or high school students who are studying data science and artificial intelligence for the first time. We will teach you the concepts of artificial intelligence and machine learning and what efforts you need to make to get a related job.

Theory and practice are different. We will grasp the basic concepts of machine learning and introduce the core concepts and theories of various essential models. Furthermore, by working with diverse datasets, we will share various techniques and know-how that are helpful in practice.

This is a PDF textbook that covers the basic grammar of R, frequently used functions, and useful intermediate to advanced techniques.

This course is for those who want to study machine learning but lack statistical knowledge.

This course covers basic natural language processing techniques and various text tasks using deep learning.

You will learn how to collect and manage all stocks listed on the stock market. Through automation, you will collect stock prices daily and even create a dashboard using Shiny to identify stock trends by industry.

Learn about GAN (Generative Adversarial Networks) in an easy and accurate way.

Learn MLP, CNN, and RNN, the basic framework of deep learning, quickly and easily.

We will learn about Q-learning and Deep Q-learning, and have time to implement reinforcement learning in R. We will cover the entire reinforcement learning content, from Deep Q-network to Self-imitation learning and Random Network Distillation.

This is a lecture that teaches you how to do data science through R. You will learn line by line, from loading data to building models and model performance strategies.

We cover frequently used functions for R programming, how to write code efficiently, and intermediate techniques.