machine-learning

Why we included this project

Machine learning beginners who want more than a pile of scattered links will find this repo's real value in its structure. The maintainer, Teddy Lee, collected free lectures, blog posts, and Jupyter notebooks and ordered them the way a study group would actually work through them: Python, Pandas, NumPy, and visualization first, then the math and statistics, then machine learning and deep learning. Notes scattered through the collection explain difficulty and ordering, so you rarely wonder whether a tutorial is worth your time or where it fits in the sequence. TensorFlow and PyTorch examples both appear, along with NLP and GAN material, which helps when you are still deciding which framework to commit to. Because it grew with community contributions, it reads like a shared study companion rather than a single author's syllabus.