Machine Learning Foundations: A Case Study Approach
Develop expertise in practical machine learning applications through comprehensive real-world case studies using Python and Jupyter notebook implementations.
Develop expertise in practical machine learning applications through comprehensive real-world case studies using Python and Jupyter notebook implementations.
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 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.
4.6
(13,434 ratings)
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Apply machine learning methods to real-world problems
Implement regression and classification models
Build recommender systems and clustering solutions
Create document retrieval systems
Develop deep learning image classifiers
Evaluate model performance and quality
Skills you'll gain
This course includes:
7.2 Hours PreRecorded video
10 quizzes, 1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
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There are 7 modules in this course
This comprehensive course provides hands-on experience with machine learning through practical case studies. Students learn to predict house prices, analyze sentiment from reviews, retrieve documents, recommend products, and implement image search systems. The curriculum covers regression, classification, clustering, recommender systems, and deep learning, with a focus on practical implementation using Python and Jupyter notebooks. The course emphasizes understanding machine learning tasks, matching them to appropriate tools, and assessing output quality.
Welcome
Module 1 · 3 Hours to complete
Regression: Predicting House Prices
Module 2 · 2 Hours to complete
Classification: Analyzing Sentiment
Module 3 · 2 Hours to complete
Clustering and Similarity: Retrieving Documents
Module 4 · 2 Hours to complete
Recommending Products
Module 5 · 3 Hours to complete
Deep Learning: Searching for Images
Module 6 · 2 Hours to complete
Closing Remarks
Module 7 · 43 Minutes to complete
Fee Structure
Instructors
Leader in Machine Learning and Intelligent Applications
Carlos Guestrin is the Amazon Professor of Machine Learning at the University of Washington's Computer Science & Engineering Department. He is also the co-founder and CEO of Dato, Inc., which focuses on facilitating the development of intelligent applications utilizing large-scale machine learning. Prior to his current role, Guestrin served as the Finmeccanica Associate Professor at Carnegie Mellon University and was a senior researcher at Intel Research Lab in Berkeley.
Expert in Machine Learning and Bayesian Modeling
Emily Fox is an assistant professor and the Amazon Professor of Machine Learning in the Statistics Department at the University of Washington. Previously, she was a faculty member in the Wharton Statistics Department at the University of Pennsylvania. Fox has received several prestigious awards, including the Sloan Research Fellowship, a Young Investigator Award from the U.S. Office of Naval Research, and a National Science Foundation CAREER Award.
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4.6 course rating
13,434 ratings
Frequently asked questions
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