University of California San Diego

Deploying Machine Learning Models

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

Taught in English

Some content may not be translated

Ilkay Altintas
Julian McAuley

Instructors: Ilkay Altintas

10,199 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

3.5

(51 reviews)

10 hours (approximately)
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

Details to know

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Assessments

8 quizzes

Course

Gain insight into a topic and learn the fundamentals

3.5

(51 reviews)

10 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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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
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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 quizzes2 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 quizzes

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 quizzes

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 Courses485,103 learners
Julian McAuley
University of California San Diego
5 Courses30,178 learners

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Recommended if you're interested in Data Analysis

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3.5

51 reviews

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ML
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Reviewed on Dec 6, 2020

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