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Supervised, Unsupervised & Reinforcement Courses by Udacity

Overview
Provider

Udacity

Dates

Opened on-going enrollment

Duration

4 months

Location

Online

Price_Front_Page

Free

Price

Free

Type

Online course

Language

English

Requirements

A strong familiarity with Probability Theory, Linear Algebra and Statistics is required. An understanding of [Intro to Statistics](https://www.udacity.com/course/st101), especially [Lessons 8, 9 and 10](https://www.udacity.com/course/viewer#!/c-st101/l-48738235/m-48688822), would be helpful.

Students should also have some experience in programming (perhaps through [Introduction to CS](https://www.udacity.com/course/cs101)) and a familiarity with Neural Networks (as covered in [Introduction to Artificial Intelligence](https://www.udacity.com/course/cs271)).

Image Credits

Proftrack

You will learn about and practice a variety of Supervised, Unsupervised and Reinforcement Learning approaches.

Supervised Learning is an important component of all kinds of technologies, from stopping credit card fraud, to finding faces in camera images, to recognizing spoken language. Our goal is to give you the skills that you need to understand these technologies and interpret their output, which is important for solving a range of data science problems. And for surviving a robot uprising.

Closely related to pattern recognition, Unsupervised Learning is about analyzing data and looking for patterns. It is an extremely powerful tool for identifying structure in data. This section focuses on how you can use Unsupervised Learning approaches — including randomized optimization, clustering, and feature selection and transformation — to find structure in unlabeled data.

Reinforcement Learning is the area of Machine Learning concerned with the actions that software agents ought to take in a particular environment in order to maximize rewards. You can apply Reinforcement Learning to robot control, chess, backgammon, checkers, and other activities that a software agent can learn. Reinforcement Learning uses behaviorist psychology in order to achieve reward maximization. This section also includes important Reinforcement Learning approaches like Markov Decision Processes and Game Theory.

Supervised Learning
– Lesson 0: Machine Learning is the ROX
– Lesson 1: Decision Trees
– Lesson 2: Regression and Classification
– Lesson 3: Neural Networks
– Lesson 4: Instance-Based Learning
– Lesson 5: Ensemble B&B
– Lesson 6: Kernel Methods and Support Vector Machines (SVM)s
– Lesson 7: Computational Learning Theory
– Lesson 8: VC Dimensions
– Lesson 9: Bayesian Learning
– Lesson 10: Bayesian Inference

Unsupervised Learning
– Lesson 1: Randomized optimization
– Lesson 2: Clustering
– Lesson 3: Feature Selection
– Lesson 4: Feature Transformation
– Lesson 5: Information Theory

Reinforcement Learning
– Lesson 1: Markov Decision Processes
– Lesson 2: Reinforcement Learning
– Lesson 3: Game Theory
– Lesson 4: Game Theory, Continued

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