Machine Learning Capstone

In this Machine Learning Capstone course, you will be using various Python-based machine learning libraries such as Pandas, scikit-learn, Tensorflow/Keras, to: • build a course recommender system, • analyze course related datasets, calculate cosine similarity, and create a similarity matrix, • create recommendation systems by applying your knowledge of KNN, PCA, and non-negative matrix collaborative filtering, • build similarity-based recommender systems, • predict course ratings by training a neural network and constructing regression and classification models, • build a Streamlit app that displays your work, and • share your work then evaluate your peers.

• Compare and contrast different machine learning algorithms by creating recommender systems in Python

• Develop a final project using machine learning methods and evaluate your peers’ projects

• Predict course ratings by training a neural network and constructing regression and classification models

• Create recommendation systems by applying your knowledge of KNN, PCA, and non-negative matrix collaborative filtering

Syllabus

Syllabus - What you will learn from this course
Week 1
Capstone Overview
Week 2
Exploratory Data Analysis and Feature Engineering
Week 3
Unsupervised-Learning Based Recommender System
Week 4
Supervised-Learning Based Recommender Systems
Week 5
Share and Present Your Recommender Systems
Week 6
Final Submission

FAQ

When will I have access to the lectures and assignments?

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

What are the course prerequisites?

To take this course you must have completed these five courses: Exploratory Data Analysis for Machine Learning, Supervised Machine Learning: Regression, Supervised Machine Learning: Classification, Unsupervised Machine Learning, Deep Learning and Reinforcement Learning .

Which software tools are required?

Web browser,  PowerPoint (optional), Text editor/IDE (optional), local Python runtime (optional)

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