Machine Learning Capstone

Coursera MOOC / Non-credit USD 49
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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. 

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

Skills you'll gain

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