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An Easy Introduction to Machine Learning Using scikit-learn
Introduction
You, This Course and Us (1:56)
Source Code and PDFs
Install Anaconda (2:21)
What is ML?
What is Machine Learning? (10:42)
Types of Machine Learning - Supervised Learning and Linear Regression (10:29)
Types of Machine Learning - Logistic Regression and Unsupervised Learning (8:22)
Support Vector Machines (SVMs)
What is an SVM? How do they work? (6:39)
SVM Lab (1): Loading and examining our data set (9:11)
SVM Lab (2): Building and tweaking our SVM classification model (9:08)
Decision Trees
What is a Decision Tree? (6:12)
Building a Decision Tree - Decision Tree Learning (7:43)
Building a Decision Tree - Information Gain and Gini Impurity (9:16)
Decision Trees Lab (1): Building our first Decision Tree (5:20)
Decision Trees Lab (2): Viewing and tweaking our Decision Tree (5:51)
Overfitting - the Bane of Machine Learning
What is Overfitting? And Why is it a Problem? (9:26)
Avoiding Overfitted Models - Cross Validation and Regularization (8:17)
Ensemble Learning and Random Forests
Teamwork: How Ensembles like Random Forest Mitigate the Problem of Overfitting (9:03)
Random Forest Lab: Use an Ensemble of Decision Trees to Get Better Results (4:48)
What is Overfitting? And Why is it a Problem?
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