Instructor: Alejandro Correa Bahnsen
- email: al.bahnsen@gmail.com
- twitter: @albahnsen
- github: albahnsen
This is a short version of the course Practical Machine Learning
- Python version 3.5;
- Numpy, the core numerical extensions for linear algebra and multidimensional arrays;
- Scipy, additional libraries for scientific programming;
- Matplotlib, excellent plotting and graphing libraries;
- IPython, with the additional libraries required for the notebook interface.
- Pandas, Python version of R dataframe
- scikit-learn, Machine learning library!
A good, easy to install option that supports Mac, Windows, and Linux, and that has all of these packages (and much more) is the Anaconda.
| Session | Notebook link |
|---|---|
| 1 | Introduction to Machine Learning |
| 2 | Linear Regression |
| 3 | Logistic Regression |
| 4 | Data preparation and Model Evaluation |
| 5 | Decision Trees |
| 6 | Ensemble Methods - Bagging |
| 7 | Model Deployment |