The Machine Learning Capstone allows you to showcase and strengthen your Python-based machine learning skills. In this hands-on course, you’ll integrate concepts from previous courses and apply them to real-world problems using Pandas, Scikit-learn, and TensorFlow/Keras.

Your primary project involves building a course recommender system. You’ll work with course datasets, calculate cosine similarity, generate similarity matrices, and experiment with multiple algorithms. Using K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), and non-negative matrix collaborative filtering, you’ll compare and evaluate the performance of different recommendation approaches.

In addition, you’ll train a neural network to predict course ratings and develop regression and classification models to enhance your predictive analytics expertise. This project emphasizes both technical skills and critical thinking in model evaluation and selection.

By course completion, you’ll have a portfolio-ready project, hands-on experience with advanced machine learning techniques, and the confidence to apply your skills to real-world challenges.

Learning Objectives

• Apply advanced machine learning techniques using Python libraries like Pandas, Scikit-learn, and TensorFlow/Keras.

• Design and implement a real-world course recommender system using cosine similarity, KNN, PCA, and collaborative filtering.

• Build and evaluate regression, classification, and neural network models to demonstrate predictive analytics skills.

• Highlight critical thinking and professional expertise by comparing algorithms, selecting optimal models, and completing a portfolio-ready project.

Course Syllabus

Welcome

Video: Introduction to Machine Learning Capstone
Ungraded Plugin: Capstone Overview

Module 1: Machine Learning Capstone Overview

Reading: Learning Objectives
Video: Introduction to Recommender Systems
Reading: Text Analysis
Reading: Stopwords and WordCloud
App Item: Exploratory Data Analysis on Online Course Enrollment Data
App Item: Extract Bag of Words (BoW) Features from Course Textual Content
Reading: Sparse and Dense Bag of Words (BOW) Vectors
Reading: Similarity Measures in Recommender Systems
App Item: Calculate Course Similarity using BoW Features
Practice Assignment: Checkpoints: Exploratory Data Analysis on Online Course Enrollment Data
Graded Assignment: Exploratory Data Analysis and Feature Engineering

Module 2: Unsupervised-Learning Based Recommender System

Reading: Learning Objectives
Video: Content-based Recommender Systems
Reading: Evaluation Metrics of Recommender Systems
App Item: Content-based Course Recommender System using User Profile and Course Genres
Reading: Heatmaps
App Item: Content-based Course Recommender System using Course Similarities
App Item: Clustering-based Course Recommender System
Practice Assignment: Checkpoints: Unsupervised-Learning Based Recommender System
Graded Assignment: Unsupervised-Learning Based Recommendation Systems

Module 3: Supervised-Learning Based Recommender Systems

Reading: Learning Objectives
Video: Collaborative Filtering-Based Recommender Systems
Reading: Exploring Surprise Library and KNN Model
App Item: Collaborative Filtering-based Recommender System using K Nearest Neighbor
App Item: Collaborative Filtering-based Recommender System using Non-negative Matrix Factorization
App Item: Course Rating Prediction using Neural Networks
App Item: Regression-based Rating Score Prediction Using Embedding Features
App Item: Classification-based Rating Mode Prediction using Embedding Features
Practice Assignment: Checkpoints: Supervised-Learning Based Recommender Systems
Graded Assignment: Supervised-Learning Based Recommendation Methods

Module 4: Share and Present Your Recommender Systems

Reading: Learning Objectives
Video: Elements Of A Successful Data Findings Report
Reading: Structure Of A Report
Video: Best Practices For Presenting Your Findings
(Optional) Hands-on Lab: Getting Started With PowerPoint For The Web
(Optional) Hands-on Lab: Basics of PowerPoint
(Optional) Hands-on Lab: Save your PowerPoint Presentation as PDF

Module 5: Final Submission

Reading: Learning Objectives
Final Submission Overview and Instructions
Exercise: Preparing Your Presentation (with provided slide template)
Peer Review: Submit Your Work and Review Your Peers
Reading: An Overview of the Streamlit Module
Ungraded Plugin: Introduction to Streamlit
Ungraded Plugin: Build a Course Recommender App with Streamlit
Reading: Congratulations and Next Steps
Reading: Thanks from the Course Team

General Information

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Recommended Skills Prior to Taking this Course

The following skills are required to be successful with this course:

We recommend completing all previous courses in the IBM Machine Learning Professional Certificate before starting this capstone.

Machine Learning: Capstone Project

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