Hello. I was a dropout, but I am currently working as an ML developer. I felt that I needed math to do well in AI. While I was thinking about whether I should buy and solve the special lecture for the college entrance exam again, I came across Professor Shin Kyung-sik's "Basic Algebra" lecture. It was easy to build up the overall foundation, and it was also easy to review only the necessary parts. The best part was that it was really easy to understand because it was likened to computer engineering. Of course, there were many parts that took a long time to understand. Next, I found out that he also produced a calculus lecture, so I bought it right away. As expected, it was good that he explained it in an easy-to-understand way, and he divided the chapters by concept, so it was good to study briefly during my commute to and from work. I was confused because the lecture materials were in English. However, when developing, you have to use a lot of English, and to use ML, you have to look up a lot of papers. Since he used English, I could immediately tell that "That's what I saw in the lecture" especially when understanding ML methods and papers. I thought it was a good decision to buy the lecture, and I bought the linear algebra lecture right away. Looking at Professor Shin Kyung-sik's profile, there are no lectures on integration and statistics yet. If you make one, I will buy it right away. He also uploaded a lecture on signal processing on YouTube, which was very helpful. I am also looking forward to the NumPy lecture that will be released soon.