This is the Curriculum for Learn Machine Learning in 3 months (PyTorch Curriculum) by Siraj Raval on Youtube. Beginners to Python will learn to build, train, deploy, scale & maintain modern Machine learning & Deep learning models. Each weekly assignment will teach you how to use a new concept or tool, like Docker, PyTorch, or Transformer Models. The Final Project will integrate everything you've learned into a Self Driving Car simulation. After completion, start an ML startup or find relevant work in the field. The community that learns together, wins together.
- 🤝 Social: Join our Discord channel to find a study buddy
- ✨ Interactive: Every resource is web-based with user input
- 🧑🎓 Beginner-Friendly: Build weekly projects without dependencies thanks to codespaces
- 🤖 Project-Based: Learn Computer Vision, Natural Language Processing, Time Series Forecasting, Audio Processing, & Recommender Systems
- Python, Pip, Numpy, Pandas, Seaborn, PyTorch, Replit, SQL, Jupyter, Streamlit, Gradio, HuggingFace, Airflow, GCP, AWS, Spark, Azure, Looker, Snowflake, scikit-learn, prometheus, evidently, grafana, Flask, Prefect, MongoDB, Postgres, Kafka, terraform
- Elicit to answer questions
- ExplainPaper to explain math
- Summari to explain text
- Spaces to sample demos
- CoPilot to explain code
Week 1: Python Fundamentals (Allen Downey)
Assignment: Build a Python search function for Researchers. Given a list of search terms, return a list of pages sorted by relevancy. Modify the example with your own alpha parameter.
Week 2: Data Analysis (Kaggle Learn)
Assignment: Build a Data Visualization iPython notebook for Farmers. Search Kaggle for an agricultural dataset, then visualize it 3 different ways for comparison & further analysis.
Week 3: Mathematics of Machine Learning (xaktly.com)
Assignment: Solve the Bayesian Probability Problem for Supply Chain using pencil & paper. Do so after completing each full section on Calculus, Probability, Statistics, & Matrices.
Week 4: Machine Learning Techniques (Cyrille Rossant)
Assignment: Build a Random Forest Regression Model for Real Estate. Given a dataset with many features, predict the price of houses next year in Boston
Week 1: Neural Networks (Interactive Dive into Deep Learning Book)
Assignment: Build a feedforward neural network for Retail. The network classifies images of clothing after training on a labeled dataset.
Week 2: Transformers (HuggingFace Course)
Assignment: Build a conversational transformer for Mental Health therapy. Read the full code of Mini-GPT, then train it to have a therapeutic conversation by uploading it to Google colab for training.
Week 3: Diffusers (Fast.AI Course)
Assignment: Build a design generator for Architects. Create a HuggingFace Space, select an existing image dataset, & create a web interface to generate designs.
Week 4: Deep Reinforcement Learning (Simonini Thomas)
Assignment: Train a Humanoid Robot to walk in simulation within a Jupyter Notebook for Construction projects. Generate a 10 second video of the humanoid walking.
Week 1: Design (Made with ML Course)
Assignment: Design a full-stack Medical Imaging Classification app for Doctors. Create the product requirements, design documentation, & project plan.
Week 2: Development (Full Stack Deep Learning Course)
Assignment - Deploy a full-stack text recognition app for Editors. Use any experiment tracking & model management tools you learn to build this.
Week 3: Production (DataTalks.Club ML Ops ZoomCamp)
Assignment - Deploy a full stack educational tutor chatbot for a STEM domain of your choice, i.e Biology, Machine Learning, Botany, etc.
Week 4: Data Enginering (DataTalks.Club Data Engineering ZoomCamp)
Assignment - Build a full-stack Self Driving Car Simulation app. This Javascript example is a good starting point. Integrate NLP, Computer Vision, Reinforcement Learning, & ML Ops.