Overview

Class Central Tips

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. 

Syllabus

Machine Learning Capstone Overview

In this module, you will be introduced to the idea of recommender systems. All labs in subsequent modules are based on this concept. You will also be provided with an overview of the capstone project. You will perform exploratory data analysis to find preliminary insights such as data patterns. You will also use it to check assumptions with the help of summary statistics and graphical representations of online course-related data sets such as course titles, course genres, and course enrollments. Next, you will extract a word-count vector called a “bag of words” (BoW) from course titles and descriptions. The BoW feature is probably the simplest but most effective feature characterizing textual data. It is widely used in many textual machine learning tasks. Finally, you will apply the cosine similarity measurement to calculate the course similarity using the extracted BoW feature vectors.

Unsupervised-Learning Based Recommender System

In this module, you will create three course recommendation systems using different methods. In lab 1, you will create a course recommendation system based on user profile and course genre matrices by computing an interest score for each course and recommend the courses with the highest interest scores. In the second lab, you will generate a course similarity matrix to create the recommendation system. In the third lab, you will implement a clustering-based recommender system algorithm using K-means clustering and principal component analysis based on group members’ course enrollment history. In labs four and five you will use collaborative filtering to make predictions about a user’s interest based on a collection of other users’ similar preferences. In lab 4, you will perform KNN-based collaborative filtering and in lab 5, you will use non-negative matrix factorization.

Supervised-Learning Based Recommender Systems

In this module, you will predict course ratings using neural networks. In the first lab, you will train neural networks to predict course ratings while simultaneously extracting users' and items' latent features. In lab 2, you will be given course interaction feature vectors as input data. Using regression analysis, you will calculate numerical rating scores that predict whether a student will audit or complete a course. Lab 3 is similar to lab 2 but instead of using regression you will use a classification model. You will extract user and item embedding feature vectors from a neural network. With those embedding feature vectors, you will create an interaction feature vector and use that to build a classification model. The model maps the interaction feature vector to a rating mode that predicts whether a learner will audit or complete a course.

Share and Present Your Recommender Systems

In this module, you will review guidelines and best practices for creating successful reports. As well you may wish to review instructions on creating PowerPoint presentations and how to save a PowerPoint as a PDF.

Final Submission

In this final module, you will be introduced to Streamlit and have the opportunity to build a Streamlit app to showcase your work in previous modules. You will complete your submission of screenshots from the hands-on labs for your peers to review. Once you have completed your submission you will then review the submission of one of your peers and grade their submission.