Course description
Machine Learning: Supervised Learning - E-learning from Udacity
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
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.
Pre-Requisites
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
Costs
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.