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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.

Interactive
quizzes

26

Concepts and
exercises

210+
  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
      Brilliant Premium

      Indicator Matrix

      Add this clever relationship representation to your tool kit.

    2. Included with
      Brilliant Premium

      Logistic Classification

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

    3. Included with
      Brilliant Premium

      Linear Discriminant Analysis

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

    4. Included with
      Brilliant Premium

      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
      Brilliant Premium

      Tree Regression

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

    2. Included with
      Brilliant Premium

      Tree Classification

      The basics of classification via a tree.

    3. Included with
      Brilliant Premium

      Trees: Pros, Cons, and Best Practices

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

    4. Included with
      Brilliant Premium

      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
      Brilliant Premium

      Hard Margin Support Vector Machines

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

    2. Included with
      Brilliant Premium

      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
      Brilliant Premium

      Nonlinear Decision Boundaries

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

    4. Included with
      Brilliant Premium

      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
      Brilliant Premium

      Intro To Kernels

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

    2. Included with
      Brilliant Premium

      Kernel Boundaries

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

    3. Included with
      Brilliant Premium

      Kernel Support Vector Machines

      See why SVMs are one of the best models for employing kernels.

    4. Included with
      Brilliant Premium

      Using the Kernel Trick

      Explore the power of the kernel trick, and the drawbacks and pitfalls of using kernels.