Deploying Machine Learning Models (Coursera)

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.

Course 4 of 4 in the Python Data Products for Predictive Analytics Specialization.

What You Will 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

Syllabus

WEEK 1

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

WEEK 2

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.

WEEK 3

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.

WEEK 4

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