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Geena Kim
One of the most useful areas in machine learning is discovering hidden patterns from unlabeled data. Add the fundamentals of this in-demand skill to your Data Science toolkit. In this course, we will learn selected unsupervised learning methods for dimensionality reduction, clustering, and learning latent features. We will also focus on real-world applications such as recommender systems with hands-on examples of product recommendation algorithms.
Read more
One of the most useful areas in machine learning is discovering hidden patterns from unlabeled data. Add the fundamentals of this in-demand skill to your Data Science toolkit. In this course, we will learn selected unsupervised learning methods for dimensionality reduction, clustering, and learning latent features. We will also focus on real-world applications such as recommender systems with hands-on examples of product recommendation algorithms.
Read more
One of the most useful areas in machine learning is discovering hidden patterns from unlabeled data. Add the fundamentals of this in-demand skill to your Data Science toolkit. In this course, we will learn selected unsupervised learning methods for dimensionality reduction, clustering, and learning latent features. We will also focus on real-world applications such as recommender systems with hands-on examples of product recommendation algorithms.
Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. College-level math skills, including Calculus and Linear Algebra, are needed. It is recommended, but not required, to take the first course in the specialization, Introduction to Machine Learning: Supervised Learning.
This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:
MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder
MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
Course logo image by Ryan Wallace on Unsplash.
Register for this course and see more details by visiting:
OpenCourser.com/course/xrgssc/unsupervised
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Syllabus
Unsupervised Learning Intro
Now that you have a solid foundation in Supervised Learning, we shift our attention to uncovering the hidden structure from unlabeled data. We will start with an introduction to Unsupervised Learning. In this course, the models no longer have labels to learn from. They need to make sense of the data from the observations themselves. This week we are diving into Principal Component Analysis, PCA, a foundational dimension reduction technique. When you first start learning this topic, it might not seem easy. There is undoubtedly some math involved in this section. However, PCA can be grasped conceptually, perhaps more readily than anticipated. In the Supervised Learning course, we struggled with the Curse of Dimensionality. This week, we will see how PCA can reduce the number of dimensions and improve classification/regression tasks. You will have reading, a quiz, and a Jupyter notebook lab/Peer Review to implement the PCA algorithm.
Read more
Syllabus
Unsupervised Learning Intro
Now that you have a solid foundation in Supervised Learning, we shift our attention to uncovering the hidden structure from unlabeled data. We will start with an introduction to Unsupervised Learning. In this course, the models no longer have labels to learn from. They need to make sense of the data from the observations themselves. This week we are diving into Principal Component Analysis, PCA, a foundational dimension reduction technique. When you first start learning this topic, it might not seem easy. There is undoubtedly some math involved in this section. However, PCA can be grasped conceptually, perhaps more readily than anticipated. In the Supervised Learning course, we struggled with the Curse of Dimensionality. This week, we will see how PCA can reduce the number of dimensions and improve classification/regression tasks. You will have reading, a quiz, and a Jupyter notebook lab/Peer Review to implement the PCA algorithm.
Read more
Clustering
This week, we are working with clustering, one of the most popular unsupervised learning methods. Last week, we used PCA to find a low-dimensional representation of data. Clustering, on the other hand, finds subgroups among observations. We can get a meaningful intuition of the data structure or use a procedure like Cluster-then-predict. Clustering has several applications ranging from marketing customer segmentation and advertising, identifying similar movies/music, to genomics research and disease subtypes discovery. We will focus our efforts mainly on K-means clustering and hierarchical clustering with consideration to the benefits and disadvantages of both and the choice of metrics like distance or linkage. We have reading, a quiz, and a Jupyter notebook lab/Peer Review this week.
Recommender System
This week we are working with Recommender Systems. Websites like Netflix, Amazon, and YouTube will surface personalized recommendations for movies, items, or videos. This week, we explore Recommendation Engines' strategies to predict users' likes. We will consider popularity, content-based, and collaborative filtering approaches, and what similarity metrics to use. As we work with Recommendation Systems, there are challenges, like the time complexity of operations and sparse data. This week is relatively math dense. You will have a quiz wherein you will work with different similarity metric calculations. Give yourself time for this week's Jupyter notebook lab and consider performant implementations. The Peer Review section this week is short.
Matrix Factorization
We are already at the last week of course material! Get ready for another dense math week. Last week, we learned about Recommendation Systems. We used a Neighborhood Method of Collaborative Filtering, utilizing similarity measures. Latent Factor Models, including the popular Matrix Factorization (MF), can also be used for Collaborative Filtering. A 1999 publication in Nature made Non-negative Matrix Factorization extremely popular. MF has many applications, including image analysis, text mining/topic modeling, Recommender systems, audio signal separation, analytic chemistry, and gene expression analysis. For this week, we focus on Singular Value Decomposition, Non-negative Matrix Factorization, and Approximation methods. This week, we have reading, a quiz, and a Kaggle mini-project utilizing matrix factorization to categorize news articles.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for those interested in various fields such as computer science
Demonstrates the fundamentals of unsupervised learning
Focuses on real-world applications such as recommender systems
Includes hands-on examples and labs for practical experience
Does not require prior programming knowledge
Assumes college-level math skills and recommends taking the introductory course on Supervised Learning
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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 Algorithms in Machine Learning with these
activities:
Review Concepts from Supervised Learning
Show steps
Strengthen your foundational understanding of supervised learning concepts.
Browse courses on
Supervised Learning
Show steps
-
Review the basics of supervised learning algorithms.
-
Practice applying supervised learning techniques to solve problems.
Discuss Challenges in Unsupervised Learning
Show steps
Engage with peers to explore the complexities and limitations of unsupervised learning.
Browse courses on
Unsupervised Learning
Show steps
-
Identify common challenges faced in unsupervised learning.
-
Share experiences and strategies for addressing these challenges.
PCA as a Dimensionality Reduction Tool
Show steps
Understand PCA and apply it to reduce the dimensionality of a dataset.
Browse courses on
Principal Component Analysis
Show steps
-
Gather a dataset with high dimensionality.
-
Apply PCA to the dataset.
-
Evaluate the results and visualize the reduced-dimensionality data.
Three other activities
Expand to see all activities and additional details
Show all six activities
Cluster Data with K-Means and Hierarchical Clustering
Show steps
Gain proficiency in clustering techniques and apply them to real-world data.
Browse courses on
Clustering
Show steps
-
Implement the K-Means clustering algorithm.
-
Implement the hierarchical clustering algorithm.
-
Compare and contrast the results of both algorithms on different datasets.
Design and Implement a Recommender System
Show steps
Put your understanding of recommender systems into practice and assess their performance.
Browse courses on
Recommender Systems
Show steps
-
Design the architecture of your recommender system.
-
Implement the system using a programming language.
-
Evaluate the performance of your system and make improvements.
Contribute to an Open-Source Unsupervised Learning Library
Show steps
Contribute to a community project and gain practical experience in unsupervised learning.
Browse courses on
Open Source
Show steps
-
Explore open-source libraries for unsupervised learning.
-
Identify an area where you can contribute.
-
Make a pull request to the library.
Review Concepts from Supervised Learning
Show steps
Strengthen your foundational understanding of supervised learning concepts.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Review the basics of supervised learning algorithms.
- Practice applying supervised learning techniques to solve problems.
Discuss Challenges in Unsupervised Learning
Show steps
Engage with peers to explore the complexities and limitations of unsupervised learning.
Browse courses on
Unsupervised Learning
Show steps
Show steps
Show steps
- Identify common challenges faced in unsupervised learning.
- Share experiences and strategies for addressing these challenges.
PCA as a Dimensionality Reduction Tool
Show steps
Understand PCA and apply it to reduce the dimensionality of a dataset.
Browse courses on
Principal Component Analysis
Show steps
Show steps
Show steps
- Gather a dataset with high dimensionality.
- Apply PCA to the dataset.
- Evaluate the results and visualize the reduced-dimensionality data.
Three other activities
Expand to see all activities and additional details
Show all six activities
Cluster Data with K-Means and Hierarchical Clustering
Show steps
Gain proficiency in clustering techniques and apply them to real-world data.
Browse courses on
Clustering
Show steps
Show steps
Show steps
- Implement the K-Means clustering algorithm.
- Implement the hierarchical clustering algorithm.
- Compare and contrast the results of both algorithms on different datasets.
Design and Implement a Recommender System
Show steps
Put your understanding of recommender systems into practice and assess their performance.
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Design the architecture of your recommender system.
- Implement the system using a programming language.
- Evaluate the performance of your system and make improvements.
Contribute to an Open-Source Unsupervised Learning Library
Show steps
Contribute to a community project and gain practical experience in unsupervised learning.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Explore open-source libraries for unsupervised learning.
- Identify an area where you can contribute.
- Make a pull request to the library.
Career center
Learners who complete Unsupervised Algorithms in Machine Learning will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use their understanding of math and computer science to extract meaningful insights from large datasets. By enrolling in this Unsupervised Algorithms in Machine Learning course, you will learn how to discover hidden patterns from unlabeled data, a skill that is increasingly used in fields such as data analytics. This course also focuses on real-world applications such as recommender systems with hands-on examples of product recommendation algorithms that commonly used by Data Analysts.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers apply their knowledge of algorithms, statistics, and programming to build and deploy machine learning models. This Unsupervised Algorithms in Machine Learning course can help you build a foundation in unsupervised learning methods, which are crucial for many machine learning applications. By completing the course, you will be better prepared to work on projects involving dimensionality reduction, clustering, and learning latent features.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, machine learning, and programming to extract meaningful insights from data. This Unsupervised Algorithms in Machine Learning course can provide you with a solid foundation in unsupervised learning methods, which are becoming essential for many data science tasks such as dimensionality reduction and clustering.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This Unsupervised Algorithms in Machine Learning course can help you build a foundation in unsupervised learning, which is becoming increasingly useful for tasks such as fraud detection and anomaly detection. By completing this course, you will be better prepared to work on projects involving machine learning and data analysis.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This Unsupervised Algorithms in Machine Learning course may be useful for Product Managers who want to learn how to use unsupervised learning methods to gather insights from customer data. By completing this course, you will be better prepared to make data-driven decisions about product development and marketing.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This Unsupervised Algorithms in Machine Learning course can help you build a foundation in unsupervised learning methods, which can be used for tasks such as risk management and fraud detection. By completing this course, you will be better prepared to work on projects involving financial data analysis and modeling.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. This Unsupervised Algorithms in Machine Learning course may be useful for Business Analysts who want to learn how to use unsupervised learning methods to gain insights from customer data, market research data, and other types of business data. By completing this course, you will be better prepared to make data-driven recommendations to improve business outcomes.
Market Researcher
Market Researcher
Market Researchers collect and analyze data about consumer behavior. This Unsupervised Algorithms in Machine Learning course can help you build a foundation in unsupervised learning methods, which can be used for tasks such as market segmentation and customer profiling. By completing this course, you will be better prepared to conduct market research studies and provide insights to businesses.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This Unsupervised Algorithms in Machine Learning course can help you build a foundation in unsupervised learning methods, which can be used for tasks such as data exploration and hypothesis testing. By completing this course, you will be better prepared to work on projects involving data analysis and modeling.
Data Engineer
Data Engineer
Data Engineers design and build the systems that store and process data. This Unsupervised Algorithms in Machine Learning course may be useful for Data Engineers who want to learn how to use unsupervised learning methods to improve the performance of data storage and processing systems. By completing this course, you will be better prepared to work on projects involving big data and data engineering.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. This Unsupervised Algorithms in Machine Learning course may be useful for Actuaries who want to learn how to use unsupervised learning methods to improve the accuracy of risk models. By completing this course, you will be better prepared to work on projects involving risk management and insurance.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to solve business problems. This Unsupervised Algorithms in Machine Learning course may be useful for Operations Research Analysts who want to learn how to use unsupervised learning methods to improve the efficiency of operations. By completing this course, you will be better prepared to work on projects involving supply chain management and logistics.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. This Unsupervised Algorithms in Machine Learning course may be useful for Financial Analysts who want to learn how to use unsupervised learning methods to identify undervalued stocks and other investment opportunities. By completing this course, you will be better prepared to work on projects involving investment analysis and portfolio management.
Risk Manager
Risk Manager
Risk Managers identify and assess risks to an organization. This Unsupervised Algorithms in Machine Learning course may be useful for Risk Managers who want to learn how to use unsupervised learning methods to improve risk management practices. By completing this course, you will be better prepared to work on projects involving risk assessment and mitigation.
Insurance Analyst
Insurance Analyst
Insurance Analysts use mathematical and statistical models to assess risk and uncertainty in the insurance industry. This Unsupervised Algorithms in Machine Learning course may be useful for Insurance Analysts who want to learn how to use unsupervised learning methods to improve the accuracy of insurance premiums. By completing this course, you will be better prepared to work on projects involving insurance pricing and underwriting.
For more career information including salaries, visit:
OpenCourser.com/course/xrgssc/unsupervised
Reading list
We've selected six 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 Algorithms in Machine Learning.
This classic textbook covers a wide range of machine learning methods, including unsupervised learning techniques. It provides a strong foundation in the statistical principles underlying unsupervised learning.
This textbook offers a probabilistic perspective on machine learning, covering both supervised and unsupervised learning methods. It provides a deep understanding of the theoretical foundations of unsupervised learning.
Offers a practical guide to data science for business professionals. It covers unsupervised learning techniques such as clustering and dimensionality reduction, and their applications in business.
This comprehensive textbook covers a wide range of topics in machine learning, including supervised and unsupervised learning methods. It offers a strong foundation in the mathematical and statistical principles underlying unsupervised learning.
Provides a comprehensive overview of unsupervised learning theory and algorithms, covering topics such as dimensionality reduction, clustering, and independent component analysis.
Provides a comprehensive overview of probabilistic graphical models, which are powerful tools for representing and reasoning about complex data. It covers unsupervised learning techniques such as clustering and dimensionality reduction.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/xrgssc/unsupervised
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Effort
4 weeks of study, 10 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Geena Kim
Language
English
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This course belongs to a
collection
called
Machine Learning: Theory and Hands-on Practice with Python. It's comprised of two courses:
- Introduction to Machine Learning: Supervised Learning
- Unsupervised Algorithms in Machine Learning (viewing)
Good to know
Suitable for those interested in various fields such as computer science
Demonstrates the fundamentals of unsupervised learning
Focuses on real-world applications such as recommender systems
Includes hands-on examples and labs for practical experience
Does not require prior programming knowledge
Assumes college-level math skills and recommends taking the introductory course on Supervised Learning
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