Machine Learning Capstone
About this course
This Machine Learning Capstone course uses various Python-based machine learning libraries, such as Pandas, sci-kit-learn, and Tensorflow/Keras. You will also learn to apply your machine-learning skills and demonstrate your proficiency in them. Before taking this course, you must complete all the previous courses in the IBM Machine Learning Professional Certificate. In this course, you will also learn to build a course recommender system, analyze course-related datasets, calculate cosine similarity, and create a similarity matrix. Additionally, you will generate recommendation systems by applying your knowledge of KNN, PCA, and non-negative matrix collaborative filtering. Finally, you will share your work with peers and have them evaluate it, facilitating a collaborative learning experience.
90/100
CourseAsk score
- What the provider tells you
- 39/45
- Who stands behind it
- 35/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- build a course recommender system
- analyze course-related datasets
- calculate cosine similarity
- create a similarity matrix
- implement KNN
- apply PCA
- execute non-negative matrix collaborative filtering
Course objectives
- apply machine-learning techniques to real-world problems
- demonstrate proficiency in Python-based machine learning libraries
- collaborate with peers for feedback and learning
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