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Deploying Machine Learning Models
  • Level Foundation
  • Duration 11 hours
  • Course by University of California San Diego
  • Offered by

About

In this course we will learn about Recommender Systems (which we will study for the Capstone project), and also look at deployment issues for data products. By the end of this course, you should be able to implement a working recommender system (e.g. to predict ratings, or generate lists of related products), and you should understand the tools and techniques required to deploy such a working system on real-world, large-scale datasets. This course is the final course in the Python Data Products for Predictive Analytics Specialization, building on the previous three courses (Basic Data Processing and Visualization, Design Thinking and Predictive Analytics for Data Products, and Meaningful Predictive Modeling). At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.

Modules

Course Information

    • 3 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • What do you hope to get out of taking this course?

3 Readings

  • Syllabus
  • Course Materials
  • Setting Up Your System

Recommender Systems

    • 3 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Review: Recommender Systems

3 Videos

  • Introduction to Recommender Systems
  • Recommender Systems versus Other Forms of Supervised Learning
  • Collaborative Filtering-Based Recommendation

Introduction to Latent Factor Models

    • 2 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Review: Introduction to Latent Factor Models

2 Videos

  • Latent Factor Models (Part 1)
  • Latent Factor Models (Part 2)

Week 1 Assessment

    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Recommender Systems and Latent Factor Models

1 Discussions

  • What are some possible applications of recommender systems, both today and in the future?

Similarity-Based Recommenders

    • 2 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Review: Similarity-Based Recommenders

2 Videos

  • Implementing a Similarity-Based Recommender
  • Similarity-Based Recommender for Rating Prediction

Implementing Latent Factor Models

    • 2 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Review: Implementing Latent Factor Models

2 Videos

  • Implementing a Latent Factor Model (Part 1)
  • Implementing a Latent Factor Model (Part 2)

Week 2 Assessment

    • 1 Assignment
Show info about module content

1 Assignment

  • Implementing Recommender Systems

Vids

    • 3 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Review: Flask and Django

3 Videos

  • Intro to Web Server Frameworks (in Python)
  • Intro to Django
  • Flask

1 Readings

  • Setting up Your Workspace with Docker: Django

Week 3 Assessment

    • 1 Assignment
Show info about module content

1 Assignment

  • Deploying Recommender Systems

Project 4: Recommender System

    • 2 Readings
    • 1 Discussion
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Project Submission

1 Discussions

  • What is something you learned from completing this project?

2 Readings

  • Project Description
  • How to Find a Dataset

Capstone

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Capstone Submission

1 Discussions

  • What is a piece of advice you would give to someone starting this specialization?

1 Videos

  • Description of Capstone Tasks

1 Readings

  • Capstone Overview

Auto Summary

"Deploying Machine Learning Models" is a foundational course in Data Science & AI focused on Recommender Systems and deployment of data products. Taught by Coursera, it builds on prior courses in the Python Data Products for Predictive Analytics Specialization. The course spans 660 minutes and offers hands-on experience, culminating in a capstone project. Subscription options include Starter and Professional. Ideal for learners aiming to implement and deploy machine learning systems on large datasets.

Ilkay Altintas
Instructors

Ilkay Altintas

Julian McAuley
Instructors

Julian McAuley