Machine Learning Capstone
Build advanced recommendation systems using machine learning techniques in this hands-on capstone project.
Build advanced recommendation systems using machine learning techniques in this hands-on capstone project.
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 IBM Machine Learning Professional Certificate 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.
4.5
(83 ratings)
13,110 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Create recommendation systems using multiple ML approaches
Implement neural networks for course rating prediction
Apply collaborative filtering and matrix factorization
Develop content-based and clustering recommender systems
Build interactive Streamlit applications
Present and evaluate machine learning solutions
Skills you'll gain
This course includes:
0.3 Hours PreRecorded video
6 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 5 modules in this course
This comprehensive capstone project focuses on building advanced recommendation systems using various machine learning techniques. Students apply their knowledge to create course recommender systems using methods like KNN, PCA, and collaborative filtering. The curriculum covers exploratory data analysis, feature engineering, and both supervised and unsupervised learning approaches. Through hands-on labs, learners implement neural networks for rating prediction and develop a Streamlit app to showcase their work.
Machine Learning Capstone Overview
Module 1 · 4 Hours to complete
Unsupervised-Learning Based Recommender System
Module 2 · 4 Hours to complete
Supervised-Learning Based Recommender Systems
Module 3 · 6 Hours to complete
Share and Present Your Recommender Systems
Module 4 · 1 Hours to complete
Final Submission
Module 5 · 3 Hours to complete
Fee Structure
Instructors
Data Scientist at IBM Canada Dedicated to Empowering Others in Data Science and Machine Learning
He is a data scientist on the Skills Network team at IBM Canada, where he creates a variety of engaging ML courses and projects. Currently, he is studying statistics and mathematics at the University of Toronto. He is passionate about guiding individuals on their journey through the fascinating world of data science and machine learning.
AI and Machine Learning Expert at IBM Canada
Yan Luo serves as a Data Scientist and Developer at IBM Canada, where he applies his expertise in machine learning and artificial intelligence to develop innovative cognitive applications across diverse domains including software repository mining, personalized health management, wireless networks, and digital banking. After earning his Ph.D. in Machine Learning from the University of Western Ontario, he has contributed significantly to technical education through developing and teaching multiple data science courses, including Applied Data Science Capstone, Machine Learning Capstone, and Introduction to R Programming for Data Science. His work focuses on practical applications of AI and cognitive computing, bridging the gap between theoretical machine learning concepts and real-world business solutions.
Testimonials
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4.5 course rating
83 ratings
Frequently asked questions
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