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# Machine Learning

## Advanced quantitative techniques to analyze data where humans fall short.

Machine learning swoops in where humans fail — such as when there are hundreds (or hundreds of thousands) variables to keep track of and millions (or billions, or trillions) of pieces of data to process.

This course develops the mathematical basis needed to deeply understand how problems of classification and estimation work. By the end of this course, you’ll develop the techniques needed to analyze data and apply these techniques to real-world problems.

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1. 1

### Linear Regression

Get your basics in line.

1. #### Introduction to Linear Regression

The basics of prediction with a very simple model: a line.

2. #### Statistics of Linear Regression

Dive into the math behind linear regression.

3. #### Linear Algebra in Linear Regression

Brush up on linear algebra, a key tool throughout machine learning.

4. #### Higher Dimensional Regression

What happens when you need to do a regression with more than two variables? Hyperplanes!

2. 2

### Linear Classification

Classifying both quantitative and qualitative data.

1. Included with

#### Indicator Matrix

Add this clever relationship representation to your tool kit.

2. Included with

#### Logistic Classification

Instead of giving a definitive 'yes' or 'no', this method predicts probabilities of 'yes' or 'no'.

3. Included with

#### Linear Discriminant Analysis

Explore this powerful tool for separating classes of normally distributed data.

4. Included with

#### KNN Classification

"My neighbors are my friends", as a classification algorithm.

3. 3

### Trees

Explore this versatile model and related ideas like bagging, random forests, and boosting.

1. Included with

#### Tree Regression

A versatile tool, best applied when there are strong distinctions between cases.

2. Included with

#### Tree Classification

The basics of classification via a tree.

3. Included with

#### Trees: Pros, Cons, and Best Practices

A major advantage of trees is their interpretability. What are the drawbacks?

4. Included with

#### Bagging

Reduce the model variance by averaging across many trees!

4. 4

### Support Vector Machine

Divide classes with the best possible margin of error.

1. Included with

#### Hard Margin Support Vector Machines

The wall of SVMs: you're either in or you're out.

2. Included with

#### Soft Margin Support Vector Machines

Explore this SVM that works even when some points end up on the "wrong side of the wall".

3. Included with

#### Nonlinear Decision Boundaries

Sometimes, the best wall isn't a straight line.

4. Included with

#### More than Two Classes

Learn how to combine several classifiers to handle data sets with many classes.

5. 5

### Kernels

It's time to upgrade the dot product.

1. Included with

#### Intro To Kernels

Get down the basics of this tool which helps measure the similarity of vectors.

2. Included with

#### Kernel Boundaries

Use kernels to classify new data by comparing it to existing data.

3. Included with