About this Course

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Shareable Certificate
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100% online
Start instantly and learn at your own schedule.
Intermediate Level

Calculus, Linear algebra, Python, NumPy, Pandas, Matplotlib, and Scikit-learn.

Approx. 38 hours to complete
English

What you will learn

  • Explain what unsupervised learning is, and list methods used in unsupervised learning.

  • List and explain algorithms for various matrix factorization methods, and what each is used for.

  • List and explain algorithms for various matrix factorization methods, and what each is used for.

Skills you will gain

  • Dimensionality Reduction
  • Unsupervised Learning
  • Cluster Analysis
  • Recommender Systems
  • Matrix Factorization
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level

Calculus, Linear algebra, Python, NumPy, Pandas, Matplotlib, and Scikit-learn.

Approx. 38 hours to complete
English

Offered by

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University of Colorado Boulder

Start working towards your degree

This Course is part of an online degree program offered by the University of Colorado Boulder. When you enroll in a for-credit non-degree course through the university and complete it online, it counts as credit hours towards a degree at CU-Boulder. All you have to do is apply through the university.

Syllabus - What you will learn from this course

Week
1
Week 1
9 hours to complete

Unsupervised Learning Intro

9 hours to complete
3 videos (Total 34 min), 9 readings, 4 quizzes
Week
2
Week 2
8 hours to complete

Clustering

8 hours to complete
2 videos (Total 23 min), 2 readings, 2 quizzes
Week
3
Week 3
8 hours to complete

Recommender System

8 hours to complete
4 videos (Total 37 min), 1 reading, 3 quizzes
Week
4
Week 4
14 hours to complete

Matrix Factorization

14 hours to complete
5 videos (Total 55 min), 1 reading, 2 quizzes

About the Machine Learning: Theory and Hands-on Practice with Python Specialization

Machine Learning: Theory and Hands-on Practice with Python

Frequently Asked Questions

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