This Machine Learning Capstone is designed to showcase and solidify your expertise in Python-based machine learning. In this hands-on course, you’ll bring together everything you’ve learned in previous courses in the program and apply it to real-world problems using libraries such as Pandas, Scikit-learn, and TensorFlow/Keras.

Your main project will focus on building a course recommender system. You’ll work with course-related datasets, calculate cosine similarity, create similarity matrices, and experiment with multiple algorithms. By applying K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), and non-negative matrix collaborative filtering, you will compare and contrast the performance of different machine learning approaches to recommendation systems.

Beyond recommendation systems, you'll also train a neural network to predict course ratings and build regression and classification models to enhance your predictive analytics skills. This project gives you the opportunity to demonstrate not just technical proficiency, but also critical thinking in evaluating and selecting the most effective models.

By the end of the course, you’ll have a portfolio-worthy project, practical 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 such as Pandas, Scikit-learn, and TensorFlow/Keras

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

• Demonstrate proficiency in predictive analytics by building and evaluating regression, classification, and neural network models

• Showcase critical thinking and professional skills by comparing algorithms, selecting effective models, and delivering 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

Before taking this course, please ensure you have completed all of the other 5 courses in the edx Machine Learning Professional Certificate.

To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.

Machine Learning: Capstone Project

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