Unsupervised Algorithms in Machine Learning from Coursera

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.

What's inside

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.

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.

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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

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

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Strengthen your foundational understanding of supervised learning concepts.

Browse courses on Supervised Learning

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Review the basics of supervised learning algorithms.
Practice applying supervised learning techniques to solve problems.

Discuss Challenges in Unsupervised Learning

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Engage with peers to explore the complexities and limitations of unsupervised learning.

Browse courses on Unsupervised Learning

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Identify common challenges faced in unsupervised learning.
Share experiences and strategies for addressing these challenges.

PCA as a Dimensionality Reduction Tool

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Understand PCA and apply it to reduce the dimensionality of a dataset.

Browse courses on Principal Component Analysis

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Gather a dataset with high dimensionality.
Apply PCA to the dataset.
Evaluate the results and visualize the reduced-dimensionality data.

Three other activities

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Cluster Data with K-Means and Hierarchical Clustering

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Gain proficiency in clustering techniques and apply them to real-world data.

Browse courses on Clustering

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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

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Put your understanding of recommender systems into practice and assess their performance.

Browse courses on Recommender Systems

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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

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Contribute to a community project and gain practical experience in unsupervised learning.

Browse courses on Open Source

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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 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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

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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.

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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.

See salaries and explore the career path for Quantitative 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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Market Researcher

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Data Engineer

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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Insurance Analyst

Unsupervised Algorithms in Machine Learning

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