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An Easy Introduction to Machine Learning Using scikit-learn
Introduction
You, This Course and Us
Source Code and PDFs
Install Anaconda
What is ML?
What is Machine Learning?
Types of Machine Learning - Supervised Learning and Linear Regression
Types of Machine Learning - Logistic Regression and Unsupervised Learning
Support Vector Machines (SVMs)
What is an SVM? How do they work?
SVM Lab (1): Loading and examining our data set
SVM Lab (2): Building and tweaking our SVM classification model
Decision Trees
What is a Decision Tree?
Building a Decision Tree - Decision Tree Learning
Building a Decision Tree - Information Gain and Gini Impurity
Decision Trees Lab (1): Building our first Decision Tree
Decision Trees Lab (2): Viewing and tweaking our Decision Tree
Overfitting - the Bane of Machine Learning
What is Overfitting? And Why is it a Problem?
Avoiding Overfitted Models - Cross Validation and Regularization
Ensemble Learning and Random Forests
Teamwork: How Ensembles like Random Forest Mitigate the Problem of Overfitting
Random Forest Lab: Use an Ensemble of Decision Trees to Get Better Results
SVM Lab (1): Loading and examining our data set
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