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
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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
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
Duke University
University of California San Diego
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University of California San Diego
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3.5
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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.
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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:
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