Ementorhub

Machine Learning Capstone

This course is part of IBM Machine Learning Professional Certificate

Instructors: Yan Luo +1 more

Instructor ratings

We asked all learners to give feedback on our instructors based on the quality of their teaching style.

What you'll learn

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

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

Develop a final presentation and evaluate your peers’ projects

Skills you'll gain

Statistical Analysis

Artificial Neural Networks

Scikit Learn (Machine Learning Library)

Exploratory Data Analysis

Data Presentation

Supervised Learning

Tensorflow

Applied Machine Learning

Unsupervised Learning

Python Programming

Data Analysis

Technical Communication

Regression Analysis

Machine Learning

Keras (Neural Network Library)

There are 5 modules in this course

In this course, you will also learn to build a course recommender system, analyze course-related datasets, calculate cosine similarity, and create a similarity matrix. Additionally, you will generate recommendation systems by applying your knowledge of KNN, PCA, and non-negative matrix collaborative filtering.  Finally, you will share your work with peers and have them evaluate it, facilitating a collaborative learning experience.

Unsupervised-Learning Based Recommender System

Supervised-Learning Based Recommender Systems

Share and Present Your Recommender Systems

Final Submission

Explore more from Machine Learning

Applied Data Science Capstone

Data Management Capstone Project

Advanced AI and Machine Learning Techniques and Capstone

AI Capstone Project with Deep Learning