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.
Deploying Machine Learning Models
Deploying Machine Learning Models
This course is part of Python Data Products for Predictive Analytics Specialization
Instructors: Ilkay Altintas
10,486 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
3.5
(51 reviews)
10 hours to complete
3 weeks at 3 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
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
8 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
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
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 5 modules in this course
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
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
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)Offered by
Recommended if you're interested in Data Analysis
Google Cloud
Alberta Machine Intelligence Institute
University of Minnesota
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Learner reviews
3.5
51 reviews
- 5 stars
35.29%
- 4 stars
21.56%
- 3 stars
17.64%
- 2 stars
7.84%
- 1 star
17.64%
Showing 3 of 51
ML
4
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.
New to Data Analysis? Start here.
Open new doors with Coursera Plus
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
Frequently asked questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. 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. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.