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.
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 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
- Starter file can be found here:https://github.com/Lee-Sutton/sklearn/blob/master/Linear-Regression-QUICKSTART.ipynb
- A ppt is supplied in the classification folder with the lesson plan
- In this lesson I will give a brief introduction to machine learning and classification
- We will look classification using logistic regression and support vector machines
Send us an email at lmsutton@sfu.ca or jdlui@sfu.ca 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
Great community for learning about new python libraries for Machine Learning
http://scikit-learn.org/stable/
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/
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/
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