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Machine Learning Capstone
  • Level Professional
  • Duration 19 hours
  • Course by IBM
  • Offered by

About

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.

Modules

Welcome

    • 1 Videos
    • 1 Plugin
Show info about module content

1 Videos

  • Introduction to Machine Learning Capstone

Exploratory Data Analysis and Feature Engineering

    • 1 Videos
    • 3 ExternalTool
    • 4 Plugin
    • 2 Assignment
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2 Assignment

  • Checkpoints: Exploratory Data Analysis on Online Course Enrollment Data
  • Graded: Exploratory Data Analysis and Feature Engineering

3 External Tool

  • Exploratory Data Analysis on Online Course Enrollment Data
  • Extract Bag of Words (BoW) Features from Course Textual Content
  • Calculate Course Similarity using BoW Features

1 Videos

  • Introduction to Recommender Systems

Introduction to Content-Based Recommender System

    • 1 Videos
    • 1 ExternalTool
    • 1 Plugin
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1 External Tool

  • Content-based Course Recommender System using User Profile and Course Genres

1 Videos

  • Content-based Recommender Systems

Similarity-Based Recommender Systems

    • 2 ExternalTool
    • 1 Plugin
    • 2 Assignment
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2 Assignment

  • Checkpoints: Unsupervised-Learning Based Recommender System
  • Graded: Unsupervised-Learning Based Recommendation Systems

2 External Tool

  • Content-based Course Recommender System using Course Similarities
  • Clustering-based Course Recommender System

Introduction to Collaborative Filtering Based Recommender Systems

    • 1 Videos
    • 2 ExternalTool
    • 1 Plugin
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2 External Tool

  • Collaborative Filtering-based Recommender System using K Nearest Neighbor
  • Collaborative Filtering-based Recommender System using Non-negative Matrix Factorization

1 Videos

  • Collaborative Filtering-Based Recommender Systems

Predictive Model Based Recommender Systems

    • 3 ExternalTool
    • 2 Assignment
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2 Assignment

  • Checkpoints: Supervised-Learning Based Recommender Systems
  • Graded: Supervised-Learning Based Recommendation Methods

3 External Tool

  • Course Rating Prediction using Neural Networks
  • Regression-based Rating Score Prediction Using Embedding Features
  • Classification-based Rating Mode Prediction using Embedding Features

How to Present Your Findings

    • 2 Videos
    • 4 Plugin
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2 Videos

  • Elements Of A Successful Data Findings Report
  • Best Practices For Presenting Your Findings

Final Submission and Instructions

    • 1 PeerReview
    • 2 Plugin
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1 Peer Review

  • Submit Your Work and Review Your Peers

(Optional) Deploy and Showcase Your Models

    • 1 Readings
    • 2 Plugin
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1 Readings

  • An Overview of the Streamlit Module

Course Wrap-Up

    • 2 Readings
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2 Readings

  • Congratulations and Next Steps
  • Credits and Acknowledgements

Auto Summary

The Machine Learning Capstone course by Coursera is designed for professionals in IT and Computer Science. Led by an expert instructor, it focuses on building and analyzing recommendation systems using Python libraries such as Pandas, scikit-learn, and TensorFlow/Keras. Over 1140 minutes, learners will develop skills in KNN, PCA, neural networks, and more. The course includes practical projects like creating a Streamlit app and peer evaluations. Subscription options include a Starter plan, making it accessible for those looking to advance their machine learning expertise.

Yan Luo
Instructors

Yan Luo

Artem Arutyunov
Instructors

Artem Arutyunov