Deploying Machine Learning Models

Deploying Machine Learning Models

This course is part of Python Data Products for Predictive Analytics Specialization

Instructors: Ilkay Altintas

What you'll learn

Project structure of interactive Python data applications
Python web server frameworks: (e.g.) Flask, Django, Dash
Best practices around deploying ML models and monitoring performance
Deployment scripts, serializing models, APIs

Skills you'll gain

Machine Learning

Details to know

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Assessments

8 assignments

Taught in English

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This course is part of the Python Data Products for Predictive Analytics Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 5 modules in this course

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.

Welcome to the first week of Deploying Machine Learning Models! We will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of recommender systems and differentiate it from other types of machine learning

What's included

5 videos3 readings3 assignments2 discussion prompts

5 videosTotal 53 minutes

Introduction to Recommender Systems4 minutesPreview module
Recommender Systems versus Other Forms of Supervised Learning7 minutes
Collaborative Filtering-Based Recommendation19 minutes
Latent Factor Models (Part 1)11 minutes
Latent Factor Models (Part 2)11 minutes

3 readingsTotal 30 minutes

Syllabus10 minutes
Course Materials10 minutes
Setting Up Your System10 minutes

3 assignmentsTotal 80 minutes

Recommender Systems and Latent Factor Models20 minutes
Review: Recommender Systems30 minutes
Review: Introduction to Latent Factor Models30 minutes

2 discussion promptsTotal 20 minutes

What do you hope to get out of taking this course?10 minutes
What are some possible applications of recommender systems, both today and in the future?10 minutes

This week, we will learn how to implement a similarity-based recommender, returning predictions similar to an user's given item. We will cover how to optimize these models based on gradient descent and Jaccard similarity.

What's included

4 videos3 assignments

4 videosTotal 35 minutes

Implementing a Similarity-Based Recommender9 minutesPreview module
Similarity-Based Recommender for Rating Prediction7 minutes
Implementing a Latent Factor Model (Part 1)11 minutes
Implementing a Latent Factor Model (Part 2)6 minutes

3 assignmentsTotal 19 minutes

Implementing Recommender Systems10 minutes
Review: Similarity-Based Recommenders5 minutes
Review: Implementing Latent Factor Models4 minutes

This week, we will learn about Python web server frameworks and the overall structure of interactive Python data applications. We will also cover some tips for best practices on deploying and monitoring your applications.

What's included

3 videos1 reading2 assignments

For this final project, you will build a recommender system of your own. Find a dataset, clean it, and create a predictive system from the dataset. This will help prepare you for the upcoming capstone, where you will harness your skills from all courses of this specialization into one single project!

What's included

2 readings1 peer review1 discussion prompt

Time to put all your hard work to the test! This capstone project consists of four components, each drawing from a separate course in this specialization. It's time to show off everything you've learned from this specialization.

What's included

1 video1 reading1 peer review1 discussion prompt

Instructors

Instructor ratings

4.4 (5 ratings)

Ilkay Altintas

University of California San Diego

14 Courses496,187 learners

Julian McAuley

University of California San Diego

5 Courses31,158 learners

Offered by

University of California San Diego

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3.5

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Reviewed on Dec 6, 2020

I Liked the Course in general especially the recommender component. I would seriously recommend making major improvements and clarification to the capstone project.

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