Intro to Machine Learning with Python and Scikit learn

This repository contains the code, data, and lesson plan for the Intro to Machine Learning with Python course delivered to the scientific programming group at Simon Fraser University.

Workshop Description

Python Machine Learning Bootcamp: Your First Machine Learning Steps with Regression

In this introductory look at Machine Learning you will learn about the different types of Machine Learning models and build your first regression model. You'll learn about some of the considerations to fine-tune your model and improve convergence. We'll also show you some great online resources to learn about new ML models and get great example code for your own projects.

This workshop is intended for users who are new to Machine Learning. Users should already have installed Python 2 or Python 3 before this session. We reccomend using Anaconda 2 or 3.

Lesson Plan for December 6 2016 session

• Lesson Plan will be guided by the content on the PPT. (link to be shared)
• Etherpad will also be used to help people find important links. https://etherpad.wikimedia.org/p/2016-12-06-ML

Lesson 1: Regression

• Starter file can be found here:https://github.com/Lee-Sutton/sklearn/blob/master/Linear-Regression-QUICKSTART.ipynb

Lesson 2:

• Starter file: https://github.com/Lee-Sutton/sklearn/blob/master/Overfitting-Quickstart.ipynb

Future Lessons:

Send us an email at [email protected] or [email protected] if you are interested in future sessions!

Some future SciProg workshops in Machine Learning may include:

• logistic regression/classification + SVM
• clustering
• neural networks
• decision trees + random forest + boosting + bagging
• SVD/PCA
• Deep Learning

Useful links for your Machine Learning adventures:

Scikit Learn

Great community for learning about new python libraries for Machine Learning

http://scikit-learn.org/stable/

Greg Mori's Machine Learning Course

Great introduction to various types of Machine Learning. This will also help to flex your math muscles and approach ML problems with more method.

https://www2.cs.sfu.ca/~mori/courses/cmpt726/

SFU's New Big Data Master's Program

Several courses offered in this program help you to get into ML as well. Apala Guha is teaching CMPT 733: Programming For Big Data 2

https://portal.cs.sfu.ca/portal/outlines/1171-CMPT-733-G100/

Some excellent MOOCS

Udacity's intro to machine learning course uses python with scikit-learn and it's free! https://www.udacity.com/course/intro-to-machine-learning--ud120

Coursera's Intro to machine learning with Andrew Ng. This course goes deeper into the math behind these algorithms and uses matlab. https://www.coursera.org/learn/machine-learning