The IBM Machine Learning Capstone course utilizes several Python-based machine learning libraries, including Pandas, scikit-learn, and Tensorflow/Keras. Throughout this course, students:
• Construct a course recommender system • Analyze course related datasets, calculate cosine similarity, and create a similarity matrix • Apply their understanding of KNN, PCA, and non-negative matrix collaborative filtering to develop recommendation systems • Create similarity-based recommender systems • Train a neural network and develop regression and classification models to predict course ratings • Build a Streamlit app to display their work • Share their work with others and receive peer evaluations.
By the end of this course, students will gain hands-on experience in developing machine learning systems and practical knowledge that they can apply in various fields.