IBM Machine Learning Capstone

This repository contains materials for the Machine Learning Capstone course offered by IBM.

About this Course

The Machine Learning Capstone course leverages various Python-based machine learning libraries, including Pandas, scikit-learn, and TensorFlow/Keras. It serves as a culmination of your machine learning journey, allowing you to apply and demonstrate your proficiency in the field.

Before enrolling in this course, it is essential to complete all preceding courses within the IBM Machine Learning Professional Certificate program.

Throughout this capstone, participants will:

• Build a course recommender system
• Analyze course-related datasets
• Calculate cosine similarity and create a similarity matrix
• Generate recommendation systems using techniques such as KNN, PCA, and non-negative matrix collaborative filtering

Additionally, participants will have the opportunity to share their work with peers for evaluation, fostering a collaborative learning experience.

Contents

1. lab_jupyter_eda.ipynb: Exploratory data analysis notebook.
2. lab_jupyter_content-based recommender system using user profile and course genres.ipynb: Notebook implementing a content-based recommender system using user profiles and course genres.
3. lab_jupyter_content-based recommender system using course similarity.ipynb: Notebook implementing a content-based recommender system using course similarity.
4. lab_jupyter_clustering-based recommender system.ipynb: Notebook implementing a clustering-based recommender system.
5. lab_jupyter_cf_KNN based recommender system.ipynb: Notebook implementing a collaborative filtering recommender system using KNN.
6. lab_jupyter_cf_NMF based recommender system.ipynb: Notebook implementing a collaborative filtering recommender system using non-negative matrix factorization (NMF).
7. lab_jupyter_cf_Neural Network Embedding based recommender system & Comparison.ipynb: Notebook implementing a collaborative filtering recommender system using neural network embeddings and providing comparisons.

• LICENSE: License information for the repository.
• Machine Learning Capstone Presentation PDF.pdf: Presentation slides for the capstone project.
• README.md: This file providing an overview of the repository.

Getting Started

To get started with the materials in this repository:

1. Ensure you have completed all prerequisites within the IBM Machine Learning Professional Certificate program.
2. Clone this repository to your local machine.
3. Explore the Jupyter notebooks to dive into various aspects of the capstone project.
4. Refer to the presentation PDF for an overview of the project and its findings.

Contributor

• Amanatullah Pandu Zenklinov