Basics

Python Tutorial: https://www.youtube.com/watch?v=rfscVS0vtbw&pp=ygUPcHl0aG9uIHR1dG9yaWFs

High level roadmap: https://roadmap.sh/machine-learning

To deeply learn the content, use ONE of:

• Python for Data Science and ML: https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/?couponCode=LETSLEARNNOW. You can usually get this course for under $25. If it’s listed at much higher, wait for a sale.
• Stanford’s ML Course: https://www.coursera.org/specializations/machine-learning-introduction. Recommended by a lot of my friends and professors.

GenAI

High level roadmap: Use this as a table of contents to learn AI engineering specific concepts once you have the basics down: https:/ /roadmap.sh/ai-engineer

Word2Vec: https://www.youtube.com/watch?v=viZrOnJclY0&pp=ygUId29yZDJ2ZWM%3D

Intro to RAG + Vector Databases: https://www.youtube.com/watch?v=5Y3a61o0jFQ

HuggingFace LLM Course: https://huggingface.co/learn/llm-course/en/chapter1/2?fw=pt

Advanced NLP with spaCy: https://course.spacy.io/en/

Projects

How do you build projects? My take: build something that solves a problem of your own. If you cannot do that, search up recently funded startups from YC or a16z, pick a few you like, build one or more of their features as a project. Even better: deploy it, get real users, treat it like a mini-startup.

Data sources + challenges + ideas:

• Kaggle: https://www.kaggle.com/
• HuggingFace: https://huggingface.co/datasets