Completion

CERTIFICATE

Unsupervised Algorithms in Machine Learning

Name: Unsupervised Algorithms in Machine Learning
Rating: 3.7 (11 reviews)

Master essential unsupervised learning techniques including dimensionality reduction, clustering, and matrix factorization for real-world data analysis.

Course Cost

Free course

Intermediate

Skill Level

36 Hours

Self-paced lessons

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Machine Learning: Theory and Hands-on Practice with Python Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

3.7

3,868 Enrolled

Geena Kim

English

3.7

3,868 Enrolled

English

Geena Kim

What you'll learn

Master Principal Component Analysis for dimensionality reduction

Implement clustering algorithms for pattern discovery

Build recommender systems using collaborative filtering

Apply matrix factorization techniques to real-world problems

Develop practical skills through hands-on Python projects

Skills you'll gain

Unsupervised Learning

Clustering

Dimensionality Reduction

Matrix Factorization

PCA

Recommender Systems

Python

Data Science

Machine Learning

Statistical Analysis

This course includes:

2.45 Hours PreRecorded video

6 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

Shareable certificate

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PROVIDED BY

Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

There are 4 modules in this course

This comprehensive course explores fundamental unsupervised learning methods for discovering hidden patterns in unlabeled data. Students learn key techniques including Principal Component Analysis (PCA) for dimensionality reduction, clustering algorithms for pattern discovery, and matrix factorization methods. The curriculum covers practical applications like recommender systems and text classification, with hands-on projects using Python. Topics include similarity metrics, collaborative filtering, singular value decomposition, and non-negative matrix factorization.

Unsupervised Learning Intro

Module 1 · 8 Hours to complete

An introduction to unsupervised learning focusing on Principal Component Analysis (PCA). The module covers foundational concepts of working with unlabeled data and dimension reduction techniques. Includes practical implementation of PCA algorithm to address the curse of dimensionality.

Skills you'll gain

Dimensionality Reduction

PCA Implementation

Data Visualization

Python Programming

Statistical Analysis

This course includes:

3 Videos

3 Assignments

10 Reading Materials

1 Labs

Clustering

Module 2 · 7 Hours to complete

Focuses on clustering methods for finding subgroups among observations. Covers K-means and hierarchical clustering, their applications in marketing, customer segmentation, and genomics research. Includes analysis of different clustering metrics and practical implementation.

Skills you'll gain

Cluster Analysis

K-means Clustering

Hierarchical Clustering

Distance Metrics

Data Segmentation

This course includes:

2 Videos

1 Assignments

2 Reading Materials

1 Labs

Recommender System

Module 3 · 7 Hours to complete

Explores recommendation engine strategies including popularity-based, content-based, and collaborative filtering approaches. Covers similarity metrics, time complexity challenges, and handling sparse data. Includes practical implementation of recommendation algorithms.

Skills you'll gain

Collaborative Filtering

Similarity Metrics

Recommendation Algorithms

System Scaling

Performance Optimization

This course includes:

4 Videos

1 Assignments

1 Quizzes

1 Reading Materials

Matrix Factorization

Module 4 · 13 Hours to complete

Covers advanced topics in matrix factorization including Singular Value Decomposition, Non-negative Matrix Factorization, and approximation methods. Applications include image analysis, text mining, topic modeling, and gene expression analysis. Includes a practical project on news article classification.

Skills you'll gain

Matrix Decomposition

SVD

NMF

Text Classification

Dimensionality Reduction

This course includes:

5 Videos

1 Assignments

1 Reading Materials

Fee Structure

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

3.1

Reviews

23,309

Students

Courses

Geena Kim

Dr. Geena Kim is an Adjunct Professor in the Computer Science Department at the University of Colorado Boulder, where she specializes in deep learning and machine learning. She holds a Ph.D. from UC Berkeley and has extensive experience in both academia and industry, currently serving as a Research Scientist at Amazon. Her career also includes entrepreneurial ventures and technical advisory roles for Internet of Things (IoT) startups in the Bay Area, showcasing her versatile expertise in cutting-edge technology.Dr. Kim teaches several courses that focus on machine learning techniques, including "Introduction to Deep Learning," "Introduction to Machine Learning: Supervised Learning," and "Unsupervised Algorithms in Machine Learning." Her research interests encompass deep learning, computer vision, and medical image analysis, contributing to advancements in these fields through innovative applications. With a strong commitment to education and research, Geena Kim continues to influence the next generation of computer scientists and data analysts at CU Boulder.

Intermediate

Skill Level

36 Hours

Self-paced lessons

Course Cost

Free course

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.

Unsupervised Algorithms in Machine Learning

What you'll learn

Skills you'll gain

This course includes:

Get a Completion Certificate

Get a Completion Certificate

Top companies offer this course to their employees

There are 4 modules in this course

Unsupervised Learning Intro

Skills you'll gain

This course includes:

Clustering

Skills you'll gain

This course includes:

Recommender System

Skills you'll gain

This course includes:

Matrix Factorization

Skills you'll gain

This course includes:

Fee Structure

Reviews

Faculties

Frequently asked Questions

What is a cross-listed course?

Can I take cross-listed courses to fulfill my degree requirements?

What will I get if I subscribe to this Specialization?