Machine Learning: Build Recommendation Systems
Learn practical machine learning by building a movie recommendation system. Master popular algorithms, cross-validation, and regularization techniques.
Learn practical machine learning by building a movie recommendation system. Master popular algorithms, cross-validation, and regularization techniques.
This comprehensive course from Harvard focuses on practical machine learning applications through building a movie recommendation system. Students will explore fundamental machine learning concepts, including training data utilization, predictive relationship discovery, and algorithm implementation. The course covers popular machine learning algorithms, principal component analysis, and regularization techniques. Special emphasis is placed on cross-validation to prevent overtraining and ensure robust model performance. Through hands-on experience with recommendation systems, students will gain practical skills in one of data science's most valuable techniques.
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What you'll learn
Master fundamental machine learning concepts and methodologies
Implement popular machine learning algorithms effectively
Build a functional movie recommendation system from scratch
Apply cross-validation techniques to prevent model overtraining
Understand and implement regularization in machine learning models
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course provides a practical introduction to machine learning through the development of a movie recommendation system. Students learn fundamental concepts including training data utilization, predictive modeling, and algorithm implementation. The curriculum covers popular machine learning algorithms, principal component analysis, and regularization techniques, with special emphasis on cross-validation to prevent overtraining. Through hands-on experience, students gain practical skills in implementing machine learning solutions for real-world applications.
Fee Structure
Instructor
32 Courses
Harvard Biostatistics Professor and Genomics Data Analysis Pioneer
Rafael Irizarry is a distinguished Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and Professor of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute. His expertise spans genomics, data analysis, and the R programming language. Irizarry's career has been marked by significant contributions to the field of genomics data analysis over the past two decades
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4.3 course rating
89 ratings
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