Learn Machine Learning in 3 Months (PyTorch 🔥 Curriculum)

Overview

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. Together as a learning community, we're going to help each other succeed!

Components

🤝 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

Tools Used

Python, Pip, Numpy, Pandas, Seaborn, Matplotlib, PyTorch, Replit, SQL, Jupyter, Streamlit, Gradio, HuggingFace, Airflow, GCP, AWS, Spark, Scikit-learn, Prometheus, Evidently, Grafana, Flask, Prefect, MongoDB, Postgres, Kafka, Terraform, RL-Baselines, Unity, W&B, Kubernetes, DBT

Learning Tools

Elicit to answer questions
ExplainPaper to explain math
Summari to explain text
Spaces to sample demos
CoPilot to explain code

Month 1 - Machine Learning 🔥

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: 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 3: Data Analysis (Kaggle)

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 4: Machine Learning Techniques (Cyrille Rossant)

Assignment: Build a Random Forest Regression model for Real Estate. Clean, augment, & feature engineer a dataset to predict the price of houses next year in Boston

Month 2 - Deep Learning 🔥🔥

Week 1: Neural Networks (Interactive Dive into Deep Learning Book)

Assignment: Build a simple feedforward neural network for Retail. Upload the Jupyter Notebook to Colab, modify input data, monitor how it effects accuracy

Week 2: Transformers (HuggingFace Course)

Assignment: Build a conversational transformer for Mental Health therapy. Specifically, train Mini-GPT to have a therapeutic conversation by uploading it to 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.

Month 3 - Machine Learning Operations 🔥🔥🔥

Week 1: Design (Made with ML Course)

Assignment: Design a Medical Imaging Classification app for Doctors. Create the product requirements, design documentation, & project plan.

Week 2: Development (Full Stack Deep Learning Course)

Assignment - Package a pretrained text recognition model into a TorchSript binary, wrap it in a serverless cloud function, & build a simple UI.

Week 3: Production (DataTalks.CLub ML Ops ZoomCamp)

Assignment - Deploy a pretrained model for Traffic Prediction. Generate a report that detects any feature drift between model versions.

Week 4: Data Engineering (DataTalks.CLub Data Engineering ZoomCamp)

Final - Deploy a Self Driving Car Simulation app. This Javascript example is a great starting point. Integrate NLP, Computer Vision, Reinforcement Learning, & ML Ops.

Interview Preparation Study Guide

llSourcell/LearnML

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