Hello! This repository contains every documents that I've studied in my journey into the field of machine learning (ML). As a CS student, I have planned to learn about machine learning by attending the course offered by my university. However, it was very difficult to get into this class, I knew that being able to get a seat there would be a difficult task. Since then, I decided to study machine learning by myself in case I didn't make it into the course.
In here, you can find youtube videos discussing ML that I found interesting and educational. It also contains some papers that I've read to learn more about ML. You can find my own implementations of the machine learning algorithms that I learned from Andrew Ng's machine learning course offered by Stanford University (and probably some other courses that I'm gonna take in the future).
Finally, if you like this repository and want to use it as a guide to learn machine learning, then follow this arrow
- What is machine learning? - OxfordSparks
- Hello World - Machine Learning Recipes #1
- But what *is* a Neural Network? | Deep learning, chapter 1
- Machine Learning for Flappy Bird using Neural Network & Genetic Algorithm
- Statistics 101: Understanding Covariance
- Statistics 101: Understanding Correlation
- Statistics 101: Simple Linear Regression
- Statistics 101: Multiple Regression
- Machine Learning (ML)
- Gradient Descent
- Simple Linear Regression (SLR)
- Multivariate Regression (MR)
- Logistic Regression (Logit)
- OpenIntro Statistics Teaches basic statistics, linear regression, multiple regression, and logistic regression.
- Python for Informatics Basic python programming and topics on web crawling, web scrapping, etc.
- Master Machine Learning Algorithms Great book that does not focus heavily on math but teach how machine learning algorithms work.