Professional Course

Machine Learning: Supervised Learning

Udacity, Online
48 hours
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Free E-Learning: Start Anytime! See details
Self-paced Online
48 hours
Next course start
Free E-Learning: Start Anytime! See details
Self-paced Online
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Course description

Machine Learning: Supervised Learning - E-learning from Udacity

Machine Learning: Supervised Learning

This e-learning course is designed to guide learners through Supervised Learning, a machine learning task that allows a number of impressive and useful functions regularly used in our modern world. Gain a clear understanding of the technology that makes it possible to filter spam in email, recognise voice patterns for phones and much, much more. This technology is widely used in a variety of different industries and is usefull in a range of important functions, from stopping credit care fraud and facial recognition to recognizing spoken language.

A graduate level program

This Supervised Learning course is the first of three in the Machine Level series offered in cooperation with Georgia Tech. The primary outcome from this first in the series is to build the skills necessary for understanding how these technologies work and the ability to understand their output and implications for today's greatest data science problems.

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Upcoming start dates

1 start date available

Free E-Learning: Start Anytime!

  • Self-paced Online
  • Online

Who should attend?

This intermediate level Machine Learning: Supervised Learning course is designed for development professionals hoping to gain a comprehensive understanding of the topics and methods involved in Supervised Learning.


This is the first course in a series of three on machine learning. If you are considering taking all three, this is the place to start. 

Find out if this course is right for you - request more information here!

Training content

Training topics for this Machine Learning: Supervised Learning course are broken into 10 Lessons:

Machine Learning is the ROX

  • Definition of Machine Learning
  • Supervised learning
  • Induction and deduction
  • Unsupervised learning
  • Reinforcement learning

Decision Trees

  • Classification and Regression overview
  • Classification learning
  • Example: Dating
  • Representation
  • Decision trees learning
  • Decision tree expressiveness
  • ID3 algorithm
  • ID3 bias
  • Decision trees and continuous attributes

Regression and Classification

  • Regression and function approximation
  • Linear regression and best fit
  • Order of polynomial
  • Polynomial regression
  • Cross validation

Neural Networks

  • Artificial neural networks
  • Perceptron units
  • XOR as perceptron network
  • Perceptron training
  • Gradient descent
  • Comparison of learning rules
  • Sigmoid function
  • Optimizing weights
  • Restriction bias
  • Preference bias

Instance-Based Learning

  • Instance based learning before
  • Instance based learning now
  • K-NN algorithm
  • Won’t you compute my neighbors?
  • Domain K-NNowledge
  • K-NN bias
  • Curse of dimensionality

Ensemble B&B

  • Ensemble learning: Boosting
  • Ensemble learning algorithm
  • Ensemble learning outputs
  • Weak learning
  • Boosting in code
  • When D agrees

Kernel Methods and Support Vector Machines (SVM)s

  • Support Vector Machines
  • Optimal separator
  • SVMs: Linearly married
  • Kernel methods

Computational Learning Theory

  • Computational Learning Theory
  • Learning theory
  • Resources in Machine Learning
  • Defining inductive learning
  • Teacher with constrained queries
  • Learner with constrained queries
  • Learner with mistake bounds
  • Version spaces
  • PAC learning
  • Epsilon exhausted
  • Haussler theorem

VC Dimensions

  • Infinite hypothesis spaces
  • Power of a hypothesis space
  • What does VC stand for?
  • Internal training
  • Linear separators
  • The ring
  • Polygons
  • Sampling complexity
  • VC of finite H

Bayesian Learning

  • Bayes Rule
  • Bayesian learning
  • Bayesian learning in action!
  • Noisy data
  • Best hypothesis
  • Minimum description length
  • Bayesian classification

Bayesian Inference

  • Joint distribution
  • Adding attributes
  • Conditional independence
  • Belief networks
  • Sampling from the joint distribution
  • Recovering the joint distribution
  • Inferencing rules
  • Naïve Bayes
  • Why Naïve Bayes is cool


It is free to start this Machine Learning: Supervised Learning course

Estimated time for completion assuming 6 hours per week: Approx. 2 months

2-Week Free Trial: Love it or Leave it

All Udacity courses are offered with a two-week free trial. Learners will have plenty of time to make sure that the program fits their needs. If it's not working out for any reason - user can cancel their subscription fee of charge.

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