Deploying Machine Learning Models

University of California, San Diego via Coursera

Coursera based on 51 ratings

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Overview

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...

Syllabus

Introduction

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

Implementing Recommender Systems

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.

Deploying Recommender Systems

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.

Project 4: Recommender System

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 prepare you for the upcoming capstone, where you will harness your skills from all courses of this specialization into one single project!

Capstone

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.