Machine Learning Self-Study Repository

Welcome to my Machine Learning Self-Study repository! This repository encapsulates my continued exploration of machine learning, ignited by a module I completed at university. Having found a profound love for this field, I have been self-studying, delving deeper into the fascinating world of machine learning.

Purpose

The primary purpose of this repository is to serve as a learning resource for myself and potentially others who are interested in machine learning. I aim to document my progress, experiments, and the questions I have explored throughout my self-study journey.

This repository is primarily a chronicle of my learning journey, capturing my progression, experiments, and inquiries. Initially conceived for personal use, it can also serve as a resource for other enthusiastic learners drawn towards machine learning. I believe my academic beginnings and subsequent self-study could potentially guide those who wish to walk down a similar path.

Useful Links for Learning

Below are some resources that I have found to be invaluable in my own learning journey. They cover various aspects of machine learning, from basic introductions to more advanced topics. These resources are freely available to anyone interested in the subject.

• StatQuest with Josh Starmer - link - StatQuest not only demystifies complex topics in Statistics and Machine Learning, but it is also notably beneficial in helping understand a wide variety of machine learning algorithms. By breaking down these algorithms into comprehensible segments, it enhances your grasp, making these intricate processes more accessible and easier to understand.

• Google's Machine Learning Crash Course - link - This is a fast-paced, practical introduction to machine learning using TensorFlow. The course includes video lectures, text guides, and hands-on practice exercises.

• MIT's Introduction to Deep Learning - link - This course provides an introduction to deep learning, which is a powerful set of techniques driving innovations in areas like image recognition, speech and natural language processing, and more.