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Josh Bernhard ,
Michael Virgo,
Mat Leonard,
Andrew Paster,
Jennifer Staab,
Dan Romuald Mbanga,
Cezanne Camacho,
Sean Carrell,
Jay Alammar,
Luis Serrano,
and
Juan Delgado
Take Udacity's Unsupervised Machine Learning course and learn how to distill messy data into meaningful groups with unsupervised machine learning techniques.
Prerequisite details
Read more
Take Udacity's Unsupervised Machine Learning course and learn how to distill messy data into meaningful groups with unsupervised machine learning techniques.
Prerequisite details
Read more
Take Udacity's Unsupervised Machine Learning course and learn how to distill messy data into meaningful groups with unsupervised machine learning techniques.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Intermediate Python
- Basic descriptive statistics
- Basic probability
- Basic supervised machine learning
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/tmjvwk/unsupervised
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What's inside
Syllabus
Clustering is one of the most common methods of unsupervised learning. Here, we'll discuss the K-means clustering algorithm.
We continue to look at clustering methods. Here, we'll discuss hierarchical clustering and density-based clustering (DBSCAN).
Read more
Syllabus
Clustering is one of the most common methods of unsupervised learning. Here, we'll discuss the K-means clustering algorithm.
We continue to look at clustering methods. Here, we'll discuss hierarchical clustering and density-based clustering (DBSCAN).
Read more
In this lesson, we discuss Gaussian mixture model clustering. We then talk about the cluster analysis process and how to validate clustering results.
Often we need to reduce a large number of features in our data to a smaller, more relevant set. Principal Component Analysis, or PCA, is a method of feature extraction and dimensionality reduction.
In this lesson, we will look at two other methods for feature extraction and dimensionality reduction: Random Projection and Independent Component Analysis (ICA).
In this project, you'll apply your unsupervised learning skills to two demographics datasets, to identify segments and clusters in the population, and see how customers of a company map to them.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a great foundation for beginners by focusing on beginners-friendly unsupervised learning tools and techniques
Strengthens existing foundation for intermediate learners by teaching more advanced unsupervised learning techniques
Develops professional skills in unsupervised machine learning, which are highly relevant in both industry and academia
Provides hands-on labs and interactive materials, allowing learners to apply their knowledge in a practical setting
Covers unique perspectives and ideas related to unsupervised machine learning, adding depth and breadth to the topic
Requires prior knowledge of intermediate Python, basic descriptive statistics, basic probability, and supervised machine learning
Save this course
Save Unsupervised Learning to your list so you can find it easily later:
Save
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Unsupervised Learning with these
activities:
Review Introduction to Machine Learning
Show steps
Provides a solid background and introduce the key concepts of Machine Learning
View
Machine Learning, revised and updated edition...
on Amazon
Show steps
-
Read chapters 1-3
-
Complete exercises in chapters 1-3
Follow a tutorial on Gaussian mixture model clustering
Show steps
Provides step-by-step guidance and reinforces the understanding of Gaussian mixture model clustering
Show steps
-
Find a tutorial on Gaussian mixture model clustering
-
Follow the tutorial and implement the algorithm
Implement K-means clustering in Python
Show steps
Provides hands-on experience and deepens the understanding of clustering algorithms
Browse courses on
K-Means Clustering
Show steps
-
Gather and clean data
-
Implement K-means clustering algorithm
-
Evaluate the results
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve leetcode problems on unsupervised learning
Show steps
Provides additional practice and challenges to apply unsupervised learning techniques
Browse courses on
Unsupervised Learning
Show steps
-
Identify LeetCode problems related to unsupervised learning
-
Solve the problems
Attend a workshop on unsupervised learning
Show steps
Provides an opportunity to learn from experts and connect with other practitioners
Browse courses on
Unsupervised Learning
Show steps
-
Find and register for a workshop on unsupervised learning
-
Attend the workshop and actively participate
Write a blog post on Hierarchical clustering
Show steps
Encourages critical thinking and reinforces the understanding of Hierarchical clustering
Browse courses on
Hierarchical Clustering
Show steps
-
Research Hierarchical clustering
-
Write a draft of the blog post
-
Refine and publish the blog post
Mentor a junior student on unsupervised learning
Show steps
Strengthens the understanding of unsupervised learning and develops leadership skills
Browse courses on
Unsupervised Learning
Show steps
-
Find a junior student who needs help with unsupervised learning
-
Provide guidance and support on unsupervised learning concepts
Create a data visualization of PCA results
Show steps
Develops data visualization skills and enhances the understanding of dimensionality reduction techniques
Browse courses on
Principal Component Analysis
Show steps
-
Gather and clean data
-
Apply PCA to the data
-
Create a data visualization of the results
Review Introduction to Machine Learning
Show steps
Provides a solid background and introduce the key concepts of Machine Learning
View
Machine Learning, revised and updated edition...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3
- Complete exercises in chapters 1-3
Follow a tutorial on Gaussian mixture model clustering
Show steps
Provides step-by-step guidance and reinforces the understanding of Gaussian mixture model clustering
Show steps
Show steps
Show steps
- Find a tutorial on Gaussian mixture model clustering
- Follow the tutorial and implement the algorithm
Implement K-means clustering in Python
Show steps
Provides hands-on experience and deepens the understanding of clustering algorithms
Browse courses on
K-Means Clustering
Show steps
Show steps
Show steps
- Gather and clean data
- Implement K-means clustering algorithm
- Evaluate the results
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve leetcode problems on unsupervised learning
Show steps
Provides additional practice and challenges to apply unsupervised learning techniques
Browse courses on
Unsupervised Learning
Show steps
Show steps
Show steps
- Identify LeetCode problems related to unsupervised learning
- Solve the problems
Attend a workshop on unsupervised learning
Show steps
Provides an opportunity to learn from experts and connect with other practitioners
Browse courses on
Unsupervised Learning
Show steps
Show steps
Show steps
- Find and register for a workshop on unsupervised learning
- Attend the workshop and actively participate
Write a blog post on Hierarchical clustering
Show steps
Encourages critical thinking and reinforces the understanding of Hierarchical clustering
Browse courses on
Hierarchical Clustering
Show steps
Show steps
Show steps
- Research Hierarchical clustering
- Write a draft of the blog post
- Refine and publish the blog post
Mentor a junior student on unsupervised learning
Show steps
Strengthens the understanding of unsupervised learning and develops leadership skills
Browse courses on
Unsupervised Learning
Show steps
Show steps
Show steps
- Find a junior student who needs help with unsupervised learning
- Provide guidance and support on unsupervised learning concepts
Create a data visualization of PCA results
Show steps
Develops data visualization skills and enhances the understanding of dimensionality reduction techniques
Browse courses on
Principal Component Analysis
Show steps
Show steps
Show steps
- Gather and clean data
- Apply PCA to the data
- Create a data visualization of the results
Career center
Learners who complete Unsupervised Learning will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts are responsible for analyzing data to identify trends and patterns. They use a variety of techniques, including unsupervised machine learning, to extract meaningful insights from data. This course can help you build a foundation in unsupervised machine learning, which is a valuable skill for Data Analysts. The course covers topics such as clustering, dimensionality reduction, and feature extraction, which are all essential techniques for working with data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They use a variety of techniques, including unsupervised machine learning, to build models that can learn from data and make predictions. This course can help you build a foundation in unsupervised machine learning, which is a valuable skill for Machine Learning Engineers. The course covers topics such as clustering, dimensionality reduction, and feature extraction, which are all essential techniques for working with data.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data to extract meaningful insights. They use a variety of techniques, including unsupervised machine learning, to identify patterns and trends in data. This course can help you build a foundation in unsupervised machine learning, which is a valuable skill for Data Scientists. The course covers topics such as clustering, dimensionality reduction, and feature extraction, which are all essential techniques for working with data.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business data to identify opportunities and solve problems. They use a variety of techniques, including unsupervised machine learning, to understand customer behavior and market trends. This course can help you build a foundation in unsupervised machine learning, which is a valuable skill for Business Analysts. The course covers topics such as clustering, dimensionality reduction, and feature extraction, which are all essential techniques for working with data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for using mathematical and statistical techniques to analyze financial data. They use a variety of techniques, including unsupervised machine learning, to identify trading opportunities and manage risk. This course can help you build a foundation in unsupervised machine learning, which is a valuable skill for Quantitative Analysts. The course covers topics such as clustering, dimensionality reduction, and feature extraction, which are all essential techniques for working with data.
Product Manager
Product Manager
Product Managers are responsible for planning, developing, and launching new products. They use a variety of techniques, including unsupervised machine learning, to understand customer needs and market trends. This course can help you build a foundation in unsupervised machine learning, which is a valuable skill for Product Managers. The course covers topics such as clustering, dimensionality reduction, and feature extraction, which are all essential techniques for working with data.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. They use a variety of techniques, including unsupervised machine learning, to improve the performance and functionality of software applications. This course can help you build a foundation in unsupervised machine learning, which is a valuable skill for Software Engineers. The course covers topics such as clustering, dimensionality reduction, and feature extraction, which are all essential techniques for working with data.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. They use a variety of techniques, including unsupervised machine learning, to identify patterns and trends in data. This course can help you build a foundation in unsupervised machine learning, which is a valuable skill for Statisticians. The course covers topics such as clustering, dimensionality reduction, and feature extraction, which are all essential techniques for working with data.
Data Mining Engineer
Data Mining Engineer
Data Mining Engineers are responsible for extracting knowledge from data. They use a variety of techniques, including unsupervised machine learning, to identify patterns and trends in data. This course can help you build a foundation in unsupervised machine learning, which is a valuable skill for Data Mining Engineers. The course covers topics such as clustering, dimensionality reduction, and feature extraction, which are all essential techniques for working with data.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for using mathematical and statistical techniques to solve business problems. They use a variety of techniques, including unsupervised machine learning, to identify opportunities for improvement and develop solutions. This course can help you build a foundation in unsupervised machine learning, which is a valuable skill for Operations Research Analysts. The course covers topics such as clustering, dimensionality reduction, and feature extraction, which are all essential techniques for working with data.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data to make investment recommendations. They use a variety of techniques, including unsupervised machine learning, to identify trends and opportunities. This course can help you build a foundation in unsupervised machine learning, which is a valuable skill for Financial Analysts. The course covers topics such as clustering, dimensionality reduction, and feature extraction, which are all essential techniques for working with data.
Risk Analyst
Risk Analyst
Risk Analysts are responsible for identifying and assessing risks. They use a variety of techniques, including unsupervised machine learning, to identify potential risks and develop mitigation strategies. This course can help you build a foundation in unsupervised machine learning, which is a valuable skill for Risk Analysts. The course covers topics such as clustering, dimensionality reduction, and feature extraction, which are all essential techniques for working with data.
Actuary
Actuary
Actuaries are responsible for assessing and managing risks. They use a variety of techniques, including unsupervised machine learning, to develop models that predict the likelihood of events. This course can help you build a foundation in unsupervised machine learning, which is a valuable skill for Actuaries. The course covers topics such as clustering, dimensionality reduction, and feature extraction, which are all essential techniques for working with data.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts are responsible for gathering and analyzing data to provide insights to businesses. They use a variety of techniques, including unsupervised machine learning, to identify trends and opportunities. This course can help you build a foundation in unsupervised machine learning, which is a valuable skill for Business Intelligence Analysts. The course covers topics such as clustering, dimensionality reduction, and feature extraction, which are all essential techniques for working with data.
Market Researcher
Market Researcher
Market Researchers are responsible for collecting and analyzing data about markets. They use a variety of techniques, including unsupervised machine learning, to identify trends and opportunities. This course can help you build a foundation in unsupervised machine learning, which is a valuable skill for Market Researchers. The course covers topics such as clustering, dimensionality reduction, and feature extraction, which are all essential techniques for working with data.
For more career information including salaries, visit:
OpenCourser.com/course/tmjvwk/unsupervised
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Unsupervised Learning.
Provides a broad overview of unsupervised learning concepts and methods, including a discussion of the mathematical foundations of the field, with 12 chapters written by leading experts in their respective areas.
Provides a comprehensive overview of pattern recognition and machine learning, with a focus on the mathematical and statistical foundations of the field, with topics ranging from supervised learning to unsupervised learning and Bayesian inference.
Covers the mathematical foundations of sparsity and its applications in machine learning, including unsupervised learning topics such as clustering and dimensionality reduction.
Provides a comprehensive overview of dimensionality reduction techniques, including principal component analysis, singular value decomposition, and manifold learning.
Provides a mathematical foundation for information theory, machine learning, and statistical inference, with a focus on unsupervised learning topics such as clustering and dimensionality reduction.
Covers the mathematical foundations of probabilistic graphical models, with a focus on unsupervised learning algorithms such as Bayesian networks and hidden Markov models.
Provides a comprehensive overview of machine learning from a probabilistic perspective, with a focus on unsupervised learning topics such as clustering and dimensionality reduction.
Provides a comprehensive overview of Gaussian processes, with a focus on their applications in unsupervised learning, including clustering and dimensionality reduction.
Save
Provides an overview of deep learning methods for unsupervised learning, with a focus on applications in natural language processing and computer vision.
Provides an overview of reinforcement learning, with a focus on unsupervised learning techniques such as Q-learning and SARSA.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tmjvwk/unsupervised
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Effort
4 weeks
Level
Intermediate
Via
Udacity
Instructors
Josh Bernhard
Michael Virgo
Mat Leonard
Andrew Paster
Jennifer Staab
Dan Romuald Mbanga
Cezanne Camacho
Sean Carrell
Jay Alammar
Luis Serrano
Juan Delgado
Language
English
Good to know
Provides a great foundation for beginners by focusing on beginners-friendly unsupervised learning tools and techniques
Strengthens existing foundation for intermediate learners by teaching more advanced unsupervised learning techniques
Develops professional skills in unsupervised machine learning, which are highly relevant in both industry and academia
Provides hands-on labs and interactive materials, allowing learners to apply their knowledge in a practical setting
Covers unique perspectives and ideas related to unsupervised machine learning, adding depth and breadth to the topic
Requires prior knowledge of intermediate Python, basic descriptive statistics, basic probability, and supervised machine learning
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