Machine Learning: Capstone Project

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. 3b:T1492

• 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

• 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.