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Machine Learning Techniques 201

A Mathematical Overview of Machine Learning

This is the course. Make sure you've covered the basics of [statistics](../Data Science Basics 101.md) and you know what an algorithm wikipeida is before you dive in here.

Course Environment Notes
Machine Learning Octave, MATLAB Taught by Andrew Ng. Best course you can take. You can easily complete the exercises in R or Python instead of Octave or MATLAB if you prefer. Exercises can be considered optional.

Existing MOOC Specializations

Course Environments Length Notes
Machine Learning Python 4 courses

Specific Machine Learning Areas

What follows are courses on specific machine learning areas. You do not need to go through each course. Pick a few that look interesting to you.

Regression

This is the "original" machine learning technique, and much of machine learning is a riff on this original concept of fitting a straight line to a bunch of data points. If you understand the concepts behind regression analysis, much of the rest of data science will fall into place for you. Example applications: finding correlations, predicting the future.

Course Environment Notes
Machine Learning: Regression Python

Classification

This is where you learn about those famous decision trees. Example applications are sentiment analysis, prediction algorthims.

Course Environement Notes
Machine Learning: Classification Python

Clustering

Clustering is the art of grouping data into similar chuncks, and hence find similar objects. Examples include recommending/finding similar documents or products.

Course Environement Notes
Machine Learing: Clustering and Retrieval Python

Natural Language Processing

Natural Language Processing is a large sub-field within machine learning. Here we learn how to analyze natural language, translate it, evaluate it's sentiment, figure out what a user means, and search a corpus of text.

Course Environment Notes
Intro to NLP Python
Applied Text Mining in Python Python

Graph analysis

Many data sets can be analysed as graphs (Facebook friends, web page links, transportation grids). These are understood as vertices (nodes, points) and edges (lines, connections) which form a graph. such a graph can be analyzed to find friends of friends, shortest route between two addresses, most referenced websites, etc.

Course Environment Notes
Social Network Analysis Python