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 |