This is a repo for all my work and notes for the Stanford Machine Learning Course taught by Andrew Ng . The notes are mine, made with LaTeX.[I decided to switch back to paper notes at week 5]. As of August 6th, 2021, I have completed the course, 🎉. (Certificate in repo)
| Week # | Readings/Lectures/Notes | Quizzes | Completion | Assignments | Completion |
|---|---|---|---|---|---|
| Week 1 | DONE | Introduction, Linear Regression, Linear Algebra | ✔️ | N/A | N/A |
| Week 2 | DONE | Linear Regression with multiple Variables, Octave/MATLAB | ✔️ | Linear Regression | ✔️ |
| Week 3 | DONE | Logistic Regression, Regularization | ✔️ | Logistic Regression | ✔️ |
| Week 4 | DONE | Neural Networks: Representation | ✔️ | Multi-class Classification and Neural Networks | ✔️ |
| Week 5 | DONE | Neural Networks: Learning | ✔️ | Neural Network Learning | ✔️ |
| Week 6 | DONE | Advice for Applying Machine Learning, Machine Learning System Design | ✔️ | Regularized Linear Regression and Bias/Variance | ✔️ |
| Week 7 | DONE | Support Vector Machines | ✔️ | Support Vector Machines | ✔️ |
| Week 8 | DONE | Unsupervised Learning, Principal Component Analysis | ✔️ | K-Means Clustering & PCA | ✔️ |
| Week 9 | DONE | Anomaly Detection, Recommender Systems | ✔️ | Anomaly Detection and Recommender Systems | ✔️ |
| Week 10 | DONE | Large-Scale Machine Learning | ✔️ | N/A | N/A |
| Week 11 | DONE | Photo OCR | ✔️ | N/A | N/A |