Deploying Machine Learning Models

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Deploying Machine Learning Models

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

Instructors: Ilkay Altintas

11,113 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

54 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

54 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

8 assignments¹

AI Graded see disclaimer

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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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 videos Total 54 minutes

Introduction to Recommender Systems 5 minutes
Recommender Systems versus Other Forms of Supervised Learning 8 minutes
Collaborative Filtering-Based Recommendation 19 minutes
Latent Factor Models (Part 1) 12 minutes
Latent Factor Models (Part 2) 11 minutes

3 readings Total 30 minutes

Syllabus 10 minutes
Course Materials 10 minutes
Setting Up Your System 10 minutes

3 assignments Total 80 minutes

Review: Recommender Systems 30 minutes
Review: Introduction to Latent Factor Models 30 minutes
Recommender Systems and Latent Factor Models 20 minutes

2 discussion prompts Total 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 videos Total 36 minutes

Implementing a Similarity-Based Recommender 9 minutes
Similarity-Based Recommender for Rating Prediction 8 minutes
Implementing a Latent Factor Model (Part 1) 12 minutes
Implementing a Latent Factor Model (Part 2) 7 minutes

3 assignments Total 19 minutes

Review: Similarity-Based Recommenders 5 minutes
Review: Implementing Latent Factor Models 4 minutes
Implementing Recommender Systems 10 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

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Instructors

Instructor ratings

(6 ratings)

Ilkay Altintas

University of California San Diego

14 Courses 514,017 learners

Julian McAuley

University of California San Diego

5 Courses 33,967 learners

Offered by

University of California San Diego

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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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Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, 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.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.