This repository contains the hands-on materials for the Quera Institute Advanced Machine Learning course. It mirrors the official curriculum with notebooks, datasets, and project deliverables aligned to the course structure.
Official course page: https://quera.org/college/landpage/9516/advanced-machine-learning
The course is designed as a practical learning path from fundamentals to applied projects. The official page highlights a large number of lessons and exercises, plus a time-boxed completion window once you reach the Data Preparation chapter. The course also includes two major projects: one focused on text/NLP and another focused on passenger data modeling and cancellation prediction.
The course is suitable if you want a structured, end-to-end path in machine learning and plan to build a portfolio with real projects. It is not ideal if you want a shallow overview or are unwilling to invest consistent weekly time.
- Basic Python programming
- Familiarity with NumPy and Pandas
- High school level mathematics
The course does not require a formal CS degree or advanced algorithmic background.
- Project Management Tips
- Data Preparation and Feature Engineering
- Regression Techniques
- Classification Algorithms
- Model Evaluation, Selection, and Regularization
- Artificial Neural Networks
- Natural Language Processing Basics
- Unsupervised Learning and Clustering
1. Data preparation— Data Preparation2. Feature engineering— Feature Engineering3. Regression— Regression Techniques4. Classification— Classification Algorithms5. Ensemble Learning— Model Evaluation, Selection, and Regularization6. Project 1— Applied Project 17. Neural Network— Artificial Neural Networks8. Unsupervised Learning— Unsupervised Learning and Clustering9. Project 2— Natural Language Processing Basics (Applied Project)10. Learn more— Advanced Topics and Extensions
- Install dependencies from
requirements.txt. - Launch Jupyter or JupyterLab.
- Start with
1. Data preparationand proceed in order. - Each practice/project contains a
notebooksfolder and adatafolder. - Run notebook cells sequentially to reproduce results and generate outputs.
Many practices and projects generate submission.csv files. These are formatted for evaluation, grading, or leaderboard submission.
Some course materials such as video lessons and theory-only chapters (e.g., project management tips) are hosted directly on the Quera platform and may not appear as files in this repository.