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Unsupervised Algorithms in Machine Learning
  • Level Professional
  • المدة 38 ساعات hours
  • الطبع بواسطة University of Colorado Boulder
  • Offered by

عن

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.

الوحدات

Introduction

    • 1 Videos
    • 4 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • USL Introduction

1 Videos

  • Unsupervised Learning Introduction

4 Readings

  • Earn Academic Credit for your Work!
  • Course Support
  • Pre-Requisite Course Knowledge
  • Course Textbooks

Programming Assignments

    • 1 Readings
    • 1 Quiz
Show info about module content

1 Readings

  • Things of Note for Programming Assignments

1 Quiz

  • Programming Assignments Quiz

Peer Review and Honor Code Expectations

    • 2 Readings
    • 1 Discussion
    • 1 Quiz
Show info about module content

1 Discussions

  • Peer Review Expectations

2 Readings

  • Information on Peer Reviews
  • Honor Code

1 Quiz

  • Honor Code Quiz

Slide Decks

    • 1 Readings
Show info about module content

1 Readings

  • Module 1 Slides

PCA

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • Intuition
  • How it works

1 Readings

  • ISLR 12.2: Principal Component Analysis

Assessments

    • 1 Readings
    • 1 Quiz
    • 1 PeerReview
    • 1 Lab
Show info about module content

1 Peer Review

  • Week 1: PCA

1 Labs

  • Week 1: PCA

1 Readings

  • Fashion-MNIST Dataset

1 Quiz

  • Week 1 Quiz

Slide Decks

    • 1 Readings
Show info about module content

1 Readings

  • Module 2 Slides

Introduction to Clustering

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Clustering Introduction

1 Readings

  • ISLR 12.4: Clustering Methods

Hierarchical Clustering

    • 1 Videos
Show info about module content

1 Videos

  • Hierarchical Clustering

Assessments

    • 1 Discussion
    • 1 Quiz
    • 1 PeerReview
    • 1 Lab
Show info about module content

1 Peer Review

  • Week 2: Clustering

1 Discussions

  • Week 2: Cluster Metrics

1 Labs

  • Week 2: Clustering

1 Quiz

  • Week 2 Quiz

Slide Decks

    • 1 Readings
Show info about module content

1 Readings

  • Module 3 Slides

Introduction to Recommender Systems

    • 1 Videos
Show info about module content

1 Videos

  • Recommender System Introduction

Similarity Measures

    • 2 Videos
Show info about module content

2 Videos

  • Similarity Measures
  • Calculating Similarity Examples

Large-scale Recommender Systems

    • 1 Videos
Show info about module content

1 Videos

  • Recommender Systems in Large Scale

Assessments

    • 1 Quiz
    • 1 PeerReview
    • 1 Programming
Show info about module content

1 Programming

  • Week 3: Recommender Systems

1 Peer Review

  • Week 3: Recommender Systems

1 Quiz

  • Week 3 Quiz

Slide Decks

    • 1 Readings
Show info about module content

1 Readings

  • Module 4 Slides

Introduction to Matrix Factorization

    • 1 Videos
Show info about module content

1 Videos

  • Matrix Factorization Introduction

Singular Value Decomposition (SVD)

    • 2 Videos
Show info about module content

2 Videos

  • Eigen Decomposition
  • Singular Value Decomposition

Non-negative Matrix Factorization (NMF)

    • 2 Videos
Show info about module content

2 Videos

  • Non-negative Matrix Factorization
  • NMF Optimization

Assessments

    • 1 Quiz
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Week 4: BBC News Classification Kaggle Mini-Project

1 Quiz

  • Week 4 Quiz

Auto Summary

"Unsupervised Algorithms in Machine Learning" is an advanced course designed for those eager to delve into the fascinating world of Data Science and AI. This professional-level course, offered by Coursera, focuses on the discovery of hidden patterns in unlabeled data, an essential skill in the machine learning toolkit. Learners will explore key unsupervised learning methods, including dimensionality reduction, clustering, and learning latent features, with practical applications such as recommender systems through hands-on Python examples. Ideal for those with prior coding experience and a strong foundation in college-level math (Calculus and Linear Algebra), this course is a perfect fit for recent graduates or working professionals aiming to enhance their data science expertise. While it is beneficial to have completed the introductory course on supervised learning, it is not a mandatory prerequisite. This course also offers a pathway to academic credit, contributing to CU Boulder’s MS in Data Science or MS in Computer Science degrees available on the Coursera platform. These fully accredited programs feature short 8-week sessions and a flexible, pay-as-you-go tuition model, with admission based on performance rather than academic history. Enroll in the "Unsupervised Algorithms in Machine Learning" course to gain a competitive edge in data science, with subscription options available to suit your professional learning needs.

Geena Kim
Instructor

Geena Kim