MLJourney

Machine Learning Journey So Far

Embarking on a journey to learn machine learning, I started by enrolling in Andrew Ng's Machine Learning Specialization course.

This course proved to be highly informative, and I found Ng's teaching style particularly engaging and effective.

Alongside the course, I delved into several books, Like

The Hundred-Page Machine Learning Book

Grokking Machine Learning,

and The StatQuest Illustrated Guide To Machine Learning by Josh Starmer

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI by Mark Ryan (Author), Luca Massaron (Author)

All of these provided valuable insights into the field. After gaining a good foundation in machine learning, I shifted my focus to deep learning by taking Ng's Deep Learning Specialization.

While the material was somewhat challenging, I enhanced my understanding by also studying Deep Learning with Python, Second Edition by François Chollet, the architect behind the Keras library.

I completed reading the book Build a Large Language Model (From Scratch) by Sebastian Raschka. Very good book and explanitions are in details, not to be missed book at any cost. It shows how to build GPT-2 model from scratch.

Completed book by Louis-François Bouchard and Louie Peters. Very good explanation and and examples of building applications with RAG architecture and very good explainations of LangChain framework.

Currently reading AI Engineering: Building Applications with Foundation Models, 1st Edition by Chip Huyen.

Building Agentic AI Systems by Anjanava Biswas (Author), Wrick Talukdar (Author)