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01-IntroMachineLearning.ipynb
02-Linear_regression.ipynb
03-logistic_regression.ipynb
04-data_preparation_evaluation.ipynb
05-decision_trees.ipynb
06-EnsembleMethods_Bagging.ipynb
07-Model_Deployment.ipynb
README.md
m07_model_deployment.py

README.md

Pycon.co Tutorial "Practical Machine Learning"

Instructor: Alejandro Correa Bahnsen

This is a short version of the course Practical Machine Learning

Requiriments

  • 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.

Sessions

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