Brief Introduction
You will learn about and practice a variety of Unsupervised Learning approaches, including: randomized optimization, clustering, feature selection and transformation, and information theory. You will learn important Machine Learning methods, techniques and best practices, and will gain experience implementing them in this course through a hands-on final project in which you will be designing a movie recommendation system (just like Netflix!).Course Summary
This course teaches the fundamentals of unsupervised learning techniques in machine learning, including clustering and dimensionality reduction. Students will learn how to apply these techniques to real-world data and gain insights from them.Key Learning Points
- Understand the basics of unsupervised learning
- Learn how to apply clustering techniques to analyze data
- Discover how to use dimensionality reduction to simplify data analysis
- Gain experience using unsupervised learning techniques in real-world scenarios
Job Positions & Salaries of people who have taken this course might have
- USA: $120,000
- India: ₹1,200,000
- Spain: €45,000
- USA: $120,000
- India: ₹1,200,000
- Spain: €45,000
- USA: $140,000
- India: ₹1,800,000
- Spain: €55,000
- USA: $120,000
- India: ₹1,200,000
- Spain: €45,000
- USA: $140,000
- India: ₹1,800,000
- Spain: €55,000
- USA: $75,000
- India: ₹800,000
- Spain: €30,000
Related Topics for further study
- Clustering analysis
- Dimensionality reduction
- Unsupervised learning algorithms
- Data analysis
- Machine learning
Learning Outcomes
- Ability to apply unsupervised learning techniques to real-world data
- Understanding of clustering and dimensionality reduction algorithms
- Experience analyzing data using unsupervised learning techniques
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming language
- Familiarity with machine learning concepts
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
- Video-based
Similar Courses
- Machine Learning - Supervised Learning
- Data Analyst
Related Education Paths
- Data Analyst Nanodegree Program
- Machine Learning Engineer Nanodegree Program
- Artificial Intelligence for Trading Nanodegree Program
Related Books
Description
Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning!Requirements
- This class will assume that you have programming experience as you will be expected to work with python libraries such as numpy and scikit. A good grasp of probability and statistics is also required. Udacity's Intro to Statistics , especially Lessons 8, 9 and 10 , may be a useful refresher. An introductory course like Udacity's Introduction to Artificial Intelligence also provides a helpful background for this course. See the Technology Requirements for using Udacity.
Knowledge
- Instructor videosLearn by doing exercisesTaught by industry professionals
Outline
- lesson 1 Randomized optimization Optimization randomized Hill climbing Random restart hill climbing Simulated annealing Annealing algorithm Properties of simulated annealing Genetic algorithms GA skeleton Crossover example What have we learned MIMIC MIMIC: A probability model MIMIC: Pseudo code MIMIC: Estimating distributions Finding dependency trees Probability distribution lesson 2 Clustering Clustering and expectation maximization Basic clustering problem Single linkage clustering (SLC) Running time of SLC Issues with SLC K-means clustering K-means in Euclidean space K-means as optimization Soft clustering Maximum likelihood Gaussian Expectation Maximization (EM) Impossibility theorem lesson 3 Feature Selection Algorithms Filtering and Wrapping Speed Searching Relevance Relevance vs. Usefulness lesson 4 Feature Transformation Feature Transformation Words like Tesla Principal Components Analysis Independent Components Analysis Cocktail Party Problem Matrix Alternatives lesson 5 Information Theory History -Sending a Message Expected size of the message Information between two variables Mutual information Two Independent Coins Two Dependent Coins Kullback Leibler Divergence lesson 6 Unsupervised Learning Project
Summary of User Reviews
Learn the fundamentals of unsupervised learning with Udacity's machine learning course. Discover how to analyze big data and find patterns without labeled examples. Students have praised the course for its comprehensive approach to the subject matter.Key Aspect Users Liked About This Course
Comprehensive approach to unsupervised learningPros from User Reviews
- In-depth coverage of unsupervised learning concepts
- Real-world examples and applications
- Interactive quizzes and exercises for active learning
Cons from User Reviews
- Some users found the course to be too challenging for beginners
- Limited feedback on assignments
- Not enough hands-on coding exercises
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