This repository documents my self-learning journey toward becoming an AI/ML Engineer. It captures my experiences, notes, projects, experiments, challenges, and lessons learned throughout the journey.
The field of AI and Machine Learning offers an overwhelming number of learning resources, courses, books, and tutorials. For many beginners,especially those who are ambitious and want to deeply understand the subject,deciding where to start can be challenging. Questions such as "Should I implement algorithms from scratch?", "Should I focus on libraries like Scikit-Learn?", or "How much mathematics do I need?" often create confusion.
In this repository, I share my notes, experiments, projects, and lessons learned while studying AI/ML. The goal is not only to learn how to use machine learning tools, but also to understand the mathematics, intuition, and implementation details behind them.
This README is currently a work in progress and will continue to evolve as I progress through my AI/ML journey.
⭐ If you find this repository helpful or think it may be helpful in the future,consider giving it a star. It helps others discover the repository and motivates me to continue sharing my learning journey.
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Aurélien Géron book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow.
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Mathematics for Machine Learning (MML)by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, published by Cambridge University Press