Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in Python.
This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performa…
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (cluster…
Instructor: Prof. Erik Demaine, Dr. Jason Ku, Prof. Justin Solomon View the complete course: https://ocw.mit.edu/6-006S20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63EdVPNLG3ToM6LaEUuStEY This course is an introduction to mathematical modeling of computational problems, as…
This course explores the world of Transformers and Large Language Models (LLMs). You will learn the evolution of NLP methods, the core components of the Transformer architecture, along with how they relate to LLMs as well as techniques to enhance model performance for real-world applications. Throu…
Dive into the intriguing world of AI Transformers with this curated YouTube playlist. Featuring expert insights, educational content from leading organizations, and engaging discussions, this playlist keeps you informed about the latest breakthroughs in Transformer technology.