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Mark J Grover,
Miguel Maldonado,
Joseph Santarcangelo,
and
Xintong Li
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.
Read more
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.
Read more
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.
By the end of this course you should be able to:
Explain the kinds of problems suitable for Unsupervised Learning approaches
Explain the curse of dimensionality, and how it makes clustering difficult with many features
Describe and use common clustering and dimensionality-reduction algorithms
Try clustering points where appropriate, compare the performance of per-cluster models
Understand metrics relevant for characterizing clusters
Who should take this course?
This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting.
What skills should you have?
To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.
Register for this course and see more details by visiting:
OpenCourser.com/course/uc0oqf/unsupervised
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What's inside
Syllabus
Introduction to Unsupervised Learning and K Means
This module introduces Unsupervised Learning and its applications. One of the most common uses of Unsupervised Learning is clustering observations using k-means. In this module, you become familiar with the theory behind this algorithm, and put it in practice in a demonstration.
Read more
Syllabus
Introduction to Unsupervised Learning and K Means
This module introduces Unsupervised Learning and its applications. One of the most common uses of Unsupervised Learning is clustering observations using k-means. In this module, you become familiar with the theory behind this algorithm, and put it in practice in a demonstration.
Read more
Distance Metrics & Computational Hurdles
Selecting a Clustering Algorithm
In this module, you become familiar with some of the computational hurdles around clustering algorithms, and how different clustering implementations try to overcome them. After a brief recapitulation of common clustering algorithms, you will learn how to compare them and select the clustering technique that best suits your data.
Dimensionality Reduction
This module introduces dimensionality reduction and Principal Component Analysis, which are powerful techniques for big data, imaging, and pre-processing data.
Nonlinear and Distance-Based Dimensionality Reduction
This module introduces dimensionality reduction techniques like Kernal Principal Component Analysis and multidimensional scaling. These methods are more powerful than Principal Component Analysis in many applications.
Matrix Factorization
This module introduces matrix factorization, which is a powerful technique for big data, text mining, and pre-processing data.
Final Project
Now, you have all the tools in your toolkit to highlight your Unsupervised Learning abilities in your final project.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces Unsupervised Learning via K-Means, commonly used for clustering observations, which is valuable in business settings
Covers important topics like Distance Metrics, Curse of Dimensionality, Dimensionality Reduction, Matrix Factorization
Familiarizes learners with common clustering algorithms, empowering them to compare and select the optimal technique for their data
Emphasizes hands-on practice, enabling learners to apply Unsupervised Learning methods in practical business scenarios
Suitable for aspiring Data Scientists aiming to acquire hands-on experience with Unsupervised Machine Learning techniques in a business setting
Presumes a solid foundation in programming, Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics
Save this course
Save Unsupervised Machine Learning to your list so you can find it easily later:
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Reviews summary
Well-received course on unsupervised learning
Learners say IBM's Unsupervised Machine Learning course is well-received. They praise the instructor's teaching and the course's practical projects. However, some learners criticize the accuracy of some of the course's graded tests and quizzes.
Overwhelmingly Positive Feedback
"Awesome and wholesome explaination of the concepts"
"Great course for learning about Unsupervised Learning"
"A high quality course with lots of practical techniques"
Knowledgeable and Engaging Instructor
"They have got the best instructor!"
"Again, congrats to the instructor on the videos."
"Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code."
Valuable Hands-on Experience
"Well structured course with many examples"
"Excellent!! Easy and good way to learn unsupervised algorithms!"
"Great mix of theory and application, not too superficial and not too deep. Amazing experience!"
Errors in Graded Assessments
"Many typos and incorrect quizzes that haven't been fixed after several years."
"As usual with IBM courses, the concepts are well explained and the split between theory and demo on python is very useful. However in this specific course there are a LOT of mistakes in graded tests, which have been spotted by users for months but are unanswered by course owners in discussion forums."
"the quizzes aren't that much indicative about understanding. they need to be tougher and contain more questions."
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 Machine Learning with these
activities:
Read and review 'Unsupervised Learning' by Hastie, Tibshirani, and Friedman
Show steps
Gain a comprehensive understanding of Unsupervised Learning theory and techniques through this foundational book.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
-
Purchase or borrow the book
-
Read through each chapter thoroughly
-
Take notes and highlight important concepts
-
Complete the end-of-chapter exercises and assignments
Explore tutorials on Unsupervised Learning in Python
Show steps
Gain proficiency with practical implementation of Unsupervised Learning techniques in Python.
Browse courses on
Python
Show steps
-
Identify reputable online resources or courses offering Python tutorials
-
Follow along with tutorials covering k-means, PCA, and other relevant algorithms
-
Replicate the code examples provided in the tutorials
-
Test your understanding by applying the techniques to sample datasets
Complete practice exercises on clustering algorithms
Show steps
Practice reinforcing the underlying concepts of clustering algorithms through hands-on exercises.
Show steps
-
Select a clustering algorithm to practice, e.g. k-means
-
Implement the selected algorithm in Python or other language of choice
-
Run the algorithm on various datasets with different parameters
-
Analyze the clustering results and compare performance metrics
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a meetup or online conference on Unsupervised Learning
Show steps
Connect with professionals in the field of Unsupervised Learning and gain valuable insights.
Browse courses on
Networking
Show steps
-
Research and identify relevant meetups or conferences
-
Register for the event and actively participate in sessions
-
Engage with speakers, attendees, and fellow learners
-
Exchange knowledge, ideas, and best practices
Participate in a Kaggle competition on Unsupervised Learning
Show steps
Apply your Unsupervised Learning skills in a competitive setting, testing your abilities against peers.
Browse courses on
Kaggle
Show steps
-
Register for a Kaggle competition focused on Unsupervised Learning
-
Read and understand the competition guidelines and dataset
-
Develop a strategy for data analysis, feature engineering, and model selection
-
Implement your solution and submit your results
-
Analyze your performance and compare against others
Develop a presentation or blog post on a specific Unsupervised Learning algorithm
Show steps
Solidify your understanding of an Unsupervised Learning algorithm by presenting or writing about it.
Browse courses on
Presentation
Show steps
-
Select an Unsupervised Learning algorithm to focus on
-
Research the algorithm's theory, applications, and implementation
-
Create a presentation or blog post that clearly explains the algorithm
-
Include code examples and visual aids for better understanding
Attend a workshop on advanced Unsupervised Learning techniques
Show steps
Expand your knowledge and refine your skills in Unsupervised Learning with a specialized workshop.
Browse courses on
Advanced Techniques
Show steps
-
Find and register for a workshop that aligns with your learning goals
-
Attend the workshop and actively participate in all sessions
-
Engage with instructors and fellow attendees to maximize learning
-
Apply the techniques learned to practical projects
Contribute to an open-source Unsupervised Learning library or framework
Show steps
Engage with the wider Unsupervised Learning community and make meaningful contributions to the field.
Browse courses on
Open-Source
Show steps
-
Identify an open-source Unsupervised Learning library or framework
-
Review the project's documentation and codebase
-
Propose and implement improvements or new features
-
Collaborate with other contributors and maintainers
Read and review 'Unsupervised Learning' by Hastie, Tibshirani, and Friedman
Show steps
Gain a comprehensive understanding of Unsupervised Learning theory and techniques through this foundational book.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow the book
- Read through each chapter thoroughly
- Take notes and highlight important concepts
- Complete the end-of-chapter exercises and assignments
Explore tutorials on Unsupervised Learning in Python
Show steps
Gain proficiency with practical implementation of Unsupervised Learning techniques in Python.
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify reputable online resources or courses offering Python tutorials
- Follow along with tutorials covering k-means, PCA, and other relevant algorithms
- Replicate the code examples provided in the tutorials
- Test your understanding by applying the techniques to sample datasets
Complete practice exercises on clustering algorithms
Show steps
Practice reinforcing the underlying concepts of clustering algorithms through hands-on exercises.
Show steps
Show steps
Show steps
- Select a clustering algorithm to practice, e.g. k-means
- Implement the selected algorithm in Python or other language of choice
- Run the algorithm on various datasets with different parameters
- Analyze the clustering results and compare performance metrics
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a meetup or online conference on Unsupervised Learning
Show steps
Connect with professionals in the field of Unsupervised Learning and gain valuable insights.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Research and identify relevant meetups or conferences
- Register for the event and actively participate in sessions
- Engage with speakers, attendees, and fellow learners
- Exchange knowledge, ideas, and best practices
Participate in a Kaggle competition on Unsupervised Learning
Show steps
Apply your Unsupervised Learning skills in a competitive setting, testing your abilities against peers.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Register for a Kaggle competition focused on Unsupervised Learning
- Read and understand the competition guidelines and dataset
- Develop a strategy for data analysis, feature engineering, and model selection
- Implement your solution and submit your results
- Analyze your performance and compare against others
Develop a presentation or blog post on a specific Unsupervised Learning algorithm
Show steps
Solidify your understanding of an Unsupervised Learning algorithm by presenting or writing about it.
Browse courses on
Presentation
Show steps
Show steps
Show steps
- Select an Unsupervised Learning algorithm to focus on
- Research the algorithm's theory, applications, and implementation
- Create a presentation or blog post that clearly explains the algorithm
- Include code examples and visual aids for better understanding
Attend a workshop on advanced Unsupervised Learning techniques
Show steps
Expand your knowledge and refine your skills in Unsupervised Learning with a specialized workshop.
Browse courses on
Advanced Techniques
Show steps
Show steps
Show steps
- Find and register for a workshop that aligns with your learning goals
- Attend the workshop and actively participate in all sessions
- Engage with instructors and fellow attendees to maximize learning
- Apply the techniques learned to practical projects
Contribute to an open-source Unsupervised Learning library or framework
Show steps
Engage with the wider Unsupervised Learning community and make meaningful contributions to the field.
Browse courses on
Open-Source
Show steps
Show steps
Show steps
- Identify an open-source Unsupervised Learning library or framework
- Review the project's documentation and codebase
- Propose and implement improvements or new features
- Collaborate with other contributors and maintainers
Career center
Learners who complete Unsupervised Machine Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They use their knowledge of machine learning algorithms and statistical methods to build models that can solve complex problems. The Unsupervised Machine Learning course can help Machine Learning Engineers build a foundation in unsupervised learning, which is essential for building models that can find insights from data that does not have a target or labeled variable. The course covers a variety of clustering and dimension reduction algorithms, which are commonly used by Machine Learning Engineers to analyze data and extract meaningful insights.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data to extract meaningful insights. They use statistical methods and machine learning algorithms to build models that can predict future outcomes or identify patterns in data. The Unsupervised Machine Learning course can help Data Scientists build a foundation in unsupervised learning, which is essential for finding insights from data that does not have a target or labeled variable. The course covers a variety of clustering and dimension reduction algorithms, which are commonly used by Data Scientists to analyze data and extract meaningful insights.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. They use statistical methods and data visualization techniques to communicate their findings to stakeholders. The Unsupervised Machine Learning course can help Data Analysts build a foundation in unsupervised learning, which is essential for finding insights from data that does not have a target or labeled variable. The course covers a variety of clustering and dimension reduction algorithms, which are commonly used by Data Analysts to analyze data and extract meaningful insights.
Business Analyst
Business Analyst
Business Analysts are responsible for understanding the needs of a business and translating them into technical requirements. They work with stakeholders to gather requirements, analyze data, and develop solutions that meet the needs of the business. The Unsupervised Machine Learning course can help Business Analysts build a foundation in unsupervised learning, which is essential for finding insights from data that does not have a target or labeled variable. The course covers a variety of clustering and dimension reduction algorithms, which are commonly used by Business Analysts to analyze data and extract meaningful insights.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, interpreting, and presenting data. They use statistical methods to design experiments, analyze data, and draw conclusions. The Unsupervised Machine Learning course can help Statisticians build a foundation in unsupervised learning, which is essential for finding insights from data that does not have a target or labeled variable. The course covers a variety of clustering and dimension reduction algorithms, which are commonly used by Statisticians to analyze data and extract meaningful insights.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for using mathematical and analytical methods to solve complex problems in business and industry. They use their knowledge of mathematics, statistics, and computer science to develop models that can optimize processes and improve efficiency. The Unsupervised Machine Learning course can help Operations Research Analysts build a foundation in unsupervised learning, which is essential for finding insights from data that does not have a target or labeled variable. The course covers a variety of clustering and dimension reduction algorithms, which are commonly used by Operations Research Analysts to analyze data and extract meaningful insights.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. They use their knowledge of computer science and software engineering principles to build software that meets the needs of users. The Unsupervised Machine Learning course may be useful for Software Engineers who are interested in developing machine learning applications. The course covers a variety of clustering and dimension reduction algorithms, which can be used to build models that can find insights from data that does not have a target or labeled variable.
Computer Scientist
Computer Scientist
Computer Scientists are responsible for developing and studying the theoretical foundations of computing. They use their knowledge of mathematics, logic, and algorithms to design and analyze algorithms and data structures. The Unsupervised Machine Learning course may be useful for Computer Scientists who are interested in developing machine learning algorithms. The course covers a variety of clustering and dimension reduction algorithms, which can be used to build models that can find insights from data that does not have a target or labeled variable.
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data pipelines. They use their knowledge of data engineering principles and tools to build systems that can collect, clean, and store data. The Unsupervised Machine Learning course may be useful for Data Engineers who are interested in developing machine learning pipelines. The course covers a variety of clustering and dimension reduction algorithms, which can be used to build models that can find insights from data that does not have a target or labeled variable.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for using mathematical and statistical methods to analyze financial data. They use their knowledge of mathematics, statistics, and finance to develop models that can predict future financial performance. The Unsupervised Machine Learning course may be useful for Quantitative Analysts who are interested in developing machine learning models for financial data. The course covers a variety of clustering and dimension reduction algorithms, which can be used to build models that can find insights from data that does not have a target or labeled variable.
Actuary
Actuary
Actuaries are responsible for using mathematical and statistical methods to assess risk. They use their knowledge of mathematics, statistics, and insurance to develop models that can predict the likelihood of future events. The Unsupervised Machine Learning course may be useful for Actuaries who are interested in developing machine learning models for risk assessment. The course covers a variety of clustering and dimension reduction algorithms, which can be used to build models that can find insights from data that does not have a target or labeled variable.
Market Researcher
Market Researcher
Market Researchers are responsible for collecting, analyzing, and interpreting data about consumers and markets. They use their knowledge of research methods and data analysis to develop insights that can help businesses make better decisions. The Unsupervised Machine Learning course may be useful for Market Researchers who are interested in using machine learning to analyze market data. The course covers a variety of clustering and dimension reduction algorithms, which can be used to build models that can find insights from data that does not have a target or labeled variable.
Product Manager
Product Manager
Product Managers are responsible for overseeing the development and launch of new products. They work with cross-functional teams to gather requirements, define product specifications, and track the progress of product development. The Unsupervised Machine Learning course may be useful for Product Managers who are interested in using machine learning to improve their products. The course covers a variety of clustering and dimension reduction algorithms, which can be used to build models that can find insights from data that does not have a target or labeled variable.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and managing sales teams. They work with their teams to develop sales strategies, set sales goals, and track sales performance. The Unsupervised Machine Learning course may be useful for Sales Managers who are interested in using machine learning to improve their sales performance. The course covers a variety of clustering and dimension reduction algorithms, which can be used to build models that can find insights from data that does not have a target or labeled variable.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. They work with their teams to develop marketing strategies, create marketing content, and track marketing performance. The Unsupervised Machine Learning course may be useful for Marketing Managers who are interested in using machine learning to improve their marketing performance. The course covers a variety of clustering and dimension reduction algorithms, which can be used to build models that can find insights from data that does not have a target or labeled variable.
For more career information including salaries, visit:
OpenCourser.com/course/uc0oqf/unsupervised
Reading list
We've selected 12 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 Machine Learning.
Comprehensive reference on unsupervised learning, covering a wide range of algorithms and techniques.
Comprehensive reference on dimensionality reduction, covering a wide range of techniques.
Practical guide to machine learning, with a focus on using popular open source libraries.
Save
Comprehensive reference on deep learning, covering the most important algorithms and techniques.
Comprehensive reference on reinforcement learning, covering the most important algorithms and techniques.
Comprehensive reference on pattern recognition and machine learning.
Provides a comprehensive overview of the field of machine learning, including both supervised and unsupervised learning.
Save
Comprehensive reference on machine learning, with a focus on the algorithmic foundations.
Practical guide to machine learning, with a focus on using open source tools and techniques.
Comprehensive reference on statistical learning with sparsity, covering a wide range of techniques.
Comprehensive reference on machine learning, with a focus on the probabilistic foundations.
Comprehensive reference on information theory, inference, and learning algorithms.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/uc0oqf/unsupervised
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Level
Intermediate
Via
Coursera
Institution
IBM
Instructors
Mark J Grover
Miguel Maldonado
Joseph Santarcangelo
Xintong Li
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
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آذربایجان دیلی
This course belongs to a
collection
called
IBM Introduction to Machine Learning. It's comprised of four courses:
- Supervised Machine Learning: Regression
- Exploratory Data Analysis for Machine Learning
- Supervised Machine Learning: Classification
- Unsupervised Machine Learning (viewing)
Good to know
Introduces Unsupervised Learning via K-Means, commonly used for clustering observations, which is valuable in business settings
Covers important topics like Distance Metrics, Curse of Dimensionality, Dimensionality Reduction, Matrix Factorization
Familiarizes learners with common clustering algorithms, empowering them to compare and select the optimal technique for their data
Emphasizes hands-on practice, enabling learners to apply Unsupervised Learning methods in practical business scenarios
Suitable for aspiring Data Scientists aiming to acquire hands-on experience with Unsupervised Machine Learning techniques in a business setting
Presumes a solid foundation in programming, Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics
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