ldatb / ml-with-python Goto Github PK

You can find the course at: https://www.edx.org/course/machine-learning-with-python-a-practical-introduct

I recommend viewing the project in this order:

1. Linear Regression
2. Polynomial Regression
3. Non-Linear Regression
4. KNN (K-Nearest Neighbor)
5. Decision Tree
6. Logistic Regression
7. SVM (Support Vector Machine)
8. K-Means
9. Hierarchical Clustering
10. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
11. Content-Based Recommendation Systems
12. Collaborative Filtering
13. Final Project

The first files are pure python because I didn't knew how to use Jupyter Notebooks, from KNN and so on, I decided to search the benefits of Jupyter and decided to use it. In the future I'll update the .py files to .ipynb so it's all regular.