ashishpatel26/andrew-ng-notes

This repository is a collection of handwritten notes and Jupyter notebooks covering two of Andrew Ng's most popular online courses: the classic Machine Learning course and the Deep Learning Specialization, both hosted on Coursera. Andrew Ng is a well-known AI researcher and educator, and these courses have been taken by millions of people looking to learn the foundations of machine learning. The notes are organized into five sections matching the five courses in the Deep Learning Specialization. The first covers the basics of neural networks, explaining what they are and how they learn. The second goes into techniques for improving a neural network's performance, including how to tune settings and avoid common pitfalls. The third focuses on how to plan and organize a machine learning project sensibly. The fourth covers convolutional neural networks, a type of model commonly used for image-related tasks. The fifth addresses sequence models, which handle things like text and time-series data. Along with the written notes, the repository includes Jupyter notebooks from the original course assignments. These are interactive files where code and explanations sit side by side, allowing learners to run examples and see results directly. A combined PDF of all the deep learning notes is also available for offline reading. The README links out to corresponding YouTube playlists for each course section, so someone could read the notes while also watching the lectures. The machine learning notebooks are stored separately from the deep learning notebooks within the repository's folder structure. This is primarily a study resource, not a software tool. There is nothing to install or run as an application. It is most useful for someone who is working through Andrew Ng's courses and wants a concise written reference alongside the video lectures.