The course was systematically structured in a workflow that can be immediately applied to real-world tasks, covering everything from LLM basics to Prompts, LCEL, Agents, LangGraph, Memory, and LangSmith, which was a huge help in gaining a deep understanding of the overall architecture. The balance between theory and practice allowed complex concepts to fall into place naturally, and it was an experience that truly leveled up my AI application development capabilities.

The explanations are simple, step-by-step, and easy to understand.

I applied because I wanted to systematically learn about recommendation systems, from basics to practical application. The lectures were systematically structured, covering everything from fundamental concepts of recommendation systems (content-based, collaborative filtering, etc.) to the latest deep learning-based methods. Practical coding exercises were also included, allowing me to grasp both theory and practice simultaneously. In particular, the process of directly implementing Matrix Factorization, LightFM, and deep learning-based recommendation models was very impressive, and the Kaggle practical examples were a great help for real-world applications. The instructor's explanations were clear, and the practice code was meticulously prepared, making it easy to follow along. I highly recommend this to those who are new to recommendation systems or those preparing for practical application!