Being a machine learning software engineer with a background in Physics, I felt the lack of a CS degree had been a limiting factor in my long-term growth. To tackle it head-on, I designed this curriculum for myself with the best resources I found online, focusing on CS basics as well as full-stack development, deep learning and natural language processing.
There are general knowledge courses and project courses.
General knowledge courses are for indexing knowledge in the brain into an organized system. When facing a new problem, at least you know what relevant info to look for.
Project courses are the real learning process. Learning by doing is the only way to learn.
If you are interested in the philosophy of the creation of this curriculum, I wrote an article about it:
How I Designed My Own Full-Stack ML Engineering Degree
- Coursera: From NAND to Tetris part I
- Coursera computer networking
- Stanford course youtube playlist
- parrt cs601 Sockets
- parrt cs601 Network programming
- Socket programming in Python, 5 videos
- Udacity course
- Coursera: From NAND to Tetris part II
- Architecture of a Database System Paper
- Berkeley Course Playlist
- Book: Designing Data Intensive Applications
- Course: MIT course playlist
- Grokking the System Design Interview
- Gaurav Sen Playlist
- Uber engineering blog series
- Python Factory pattern and Message Queue
- Book: Design Patterns by Gamma et al
- Highly recommended deep learning course!!
- Course site
- Youtube playlist
- Book: Building ML Powered Applications
- Suggestion: take project course CS512 for hands-on practice after this course
- Other readings:
CS510 Luigi SageMaker course
- Great blog for prodML: mlinproduction.com
- MLOps youtube playlist
- MLOps: Setup Github Actions CI/CD for NLP project
- EPI book
- Basics: Practical Python course
- Eugene Yan's Python project setup
- RestAPI with FastAPI guide
- FastAPI MadeWithML example
- Project: DoverChat, poetry, Docker, deployed on Heroku and AWS EB
- JS Prototype
- Topics of ES6-ES8, promise, async await (20 vids)
- Nature of Code
- Physics Engine, matter.js simulation project (10 vids)
- Autonomous Agents (10 vids)
- Cellular Automata (4 vids)
- Fractals (10 vids)
- Fractal trees, space colonization project
- Build API using FastAPI to get daily and hourly weather for cities
- Frontend based on Streamlit, a Python framework for building UI for prototype projects
- Book: The Good Parts of AWS by Daniel Vassallo
- Optional course for mobile game development
- youtube video: https://youtu.be/467Doas5J6I
- Project: A sound game for instruments
- Course v4: https://course.fast.ai/
- fastbook
- Document all answers to questionnaires
- FastAI2 design paper
- Deep Tutorials for PyTorch
- Official PyTorch book: Deep Learning with PyTorch
- fastai NLP course
- Awesome visual course
- Book: Practical NLP
- Survey
- Other resources
- Fast AI course v4 NLP lecture
- @amitness: How to Learn Transformers
- Advanced course
- NLP Masterclass: Modeling Fallacies in NLP
- NLP Datasets
- Book: NLP with Pytorch
- MadewithML and dair.ai chatbot materials
- Full stack ML prototype stack: FastAPI, Streamlit, Docker, Heroku
- Quote the course description: "This course isn't just about ML. In fact, it's mostly about clean software engineering! We'll cover important concepts like versioning, testing, logging, etc. that really makes this a production-grade product."
- course page
- MadeWithML youtube channel
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent