Python Programming: Build a Recommendation Engine in Django

Python Programming: Build a Recommendation Engine in Django, available at $44.99, has an average rating of 4.25, with 43 lectures, based on 16 reviews, and has 198 subscribers.

You will learn about Learn how to integrate Django & Celery Learn how to use HTMX with Django for Dynamic Loading (no JavaScript Needed) Training a Machine Learning Model with SurpriseML and an example in Keras Build a rating system in Django with dynamic rating buttons. These ratings can be used on any Django Model Learn how to run periodic background task and/or schedule functions to run exactly when needed How to perform batch inference effectively using Django for *any* large workloads and/or ML packages How to load large datasets into a SQL database through Django Models Where to find great datasets online How to implement an "infinite" review page that will always give a new item after rating. So much more! This course is ideal for individuals who are Beyond the basics Django Developers (ie you completed a Try Django course) or Anyone interested in building powerful ML-heavy Web Applications or Anyone looking to learn about Python Celery for Worker processes or Anyone interested in building workflows that need to run along side of Django. It is particularly useful for Beyond the basics Django Developers (ie you completed a Try Django course) or Anyone interested in building powerful ML-heavy Web Applications or Anyone looking to learn about Python Celery for Worker processes or Anyone interested in building workflows that need to run along side of Django.

Enroll now: Python Programming: Build a Recommendation Engine in Django

Summary

Title: Python Programming: Build a Recommendation Engine in Django

Price: $44.99

Average Rating: 4.25

Number of Lectures: 43

Number of Published Lectures: 43

Number of Curriculum Items: 43

Number of Published Curriculum Objects: 43

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will Learn

Learn how to integrate Django & Celery
Learn how to use HTMX with Django for Dynamic Loading (no JavaScript Needed)
Training a Machine Learning Model with SurpriseML and an example in Keras
Build a rating system in Django with dynamic rating buttons. These ratings can be used on any Django Model
Learn how to run periodic background task and/or schedule functions to run exactly when needed
How to perform batch inference effectively using Django for *any* large workloads and/or ML packages
How to load large datasets into a SQL database through Django Models
Where to find great datasets online
How to implement an "infinite" review page that will always give a new item after rating.
So much more!

Who Should Attend

Beyond the basics Django Developers (ie you completed a Try Django course)
Anyone interested in building powerful ML-heavy Web Applications
Anyone looking to learn about Python Celery for Worker processes
Anyone interested in building workflows that need to run along side of Django.

Target Audiences

Beyond the basics Django Developers (ie you completed a Try Django course)
Anyone interested in building powerful ML-heavy Web Applications
Anyone looking to learn about Python Celery for Worker processes
Anyone interested in building workflows that need to run along side of Django.

Build a recommendation engine using Django & a Machine Learning technique called Collaborative Filtering.

Users will rate movies and the system will automatically recommend new ones. These recommendations will be done in batches (ie not in real time) to unlock a more scalable system for training and helping thousands and thousands of users.

For this course, we’ll use a real dataset called MovieLens; this dataset is downloaded in CSV and is used on all kinds of machine learning tutorials. What’s special about this course is you’ll load this dataset into a SQL database through a Django model. This alone might be worth watching the course as SQL databases are far more powerful than CSV files.

To do the batch inference we implement the incredibly powerful background worker process called Celery. If you haven’t used Celery before, this will be an eye opening experience and when you couple it with Django you have a truly powerful worker process that can run tasks in the background, run tasks on a schedule, or a combination of both. Tasks in Celery are simply Python functions with a special decorator.

For rating movies, we’ll be using HTMX. HTMX is a way to dynamically update content *without* reloading the page at all. I am sure you know the experience whenever you click “like” or “subscribe” , that’s what HTMX gives us without the overhead of using 1 line of JavaScript. This course shows us a practical implementation of using HTMX not just for rating movies, but also sorting them, loading them, and doing much more.

The recommendation engine in Django is really a collection of 3 parts:

Web Process: Setup up Django to collect user’s interest and provide recommendations once available.
Machine Learning Pipeline: Extract data from Django, transform it, and train a Collaborative Filtering model.
Worker Process: This is the glue. We’ll use Celery to schedule/run the trained model predictions and update data for Django-related user recommendations.

Recommended Experience

Python 3.6+ (such as 30 Days of Python)
Django 3.2+ (such as Your First Django Web Project or Try Django 3.2)
Celery with Django (such as Time & Tasks 2 or this blog post)

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome to Recommender

Lecture 2: Requirements & In-Depth Walkthrough

Lecture 3: Where to get help

Lecture 4: Setup Project

Lecture 5: Django as a ML Pipeline Orchestration Tool

Chapter 2: Handling the Dataset

Lecture 1: Generate Fake User Data

Lecture 2: Django Management Command to add Fake User Data

Lecture 3: Our Collaborative Filtering Dataset

Lecture 4: Load The Movies Dataset into the Movie Django Model

Lecture 5: Create Ratings Model with Generic Foreign Keys

Chapter 3: Running Calculations with Django

Lecture 1: Calculate Average Ratings

Lecture 2: Generate Movie Ratings

Lecture 3: Handling Duplicate Ratings with Signals

Lecture 4: Calculate Movie Average Rating Task

Chapter 4: Python Celery

Lecture 1: Setup Celery for Offloading Tasks

Lecture 2: Converting Functions into Celery Tasks

Chapter 5: Django Views & Auth

Lecture 1: Movie List & Detail View, URLs and Templates

Lecture 2: Django AllAuth

Lecture 3: Update the Movie Ratings Task

Chapter 6: User Ratings

Lecture 1: Rendering Rating Choices

Lecture 2: Display a User's Ratings

Chapter 7: Dynamic Django with HTMX

Lecture 1: Dynamic Requests with HTMX

Lecture 2: Rate Movies Dynamically with HTMX

Lecture 3: Infinite Rating Flow with Django & HTMX

Lecture 4: Rating Dataset Exports Model & Task

Chapter 8: Jupyter Notebooks with Django

Lecture 1: Using Jupyter with Django

Lecture 2: Load Real Ratings to Fake Users

Lecture 3: Update Movie Data

Lecture 4: Recommendations by Popularity

Chapter 9: Machine Learning & Collaborative Filtering

Lecture 1: What is Collaborative Filtering

Lecture 2: Collaborative Filtering with Surprise ML

Lecture 3: Surprise ML Utils & Celery Task For Surprise Model Training

Lecture 4: Batch User Prediction Task

Chapter 10: Handling Predictions in Django

Lecture 1: Storing Predictions in our Suggestion Model

Lecture 2: Updating Batch Predictions Based on Previous Suggestions

Lecture 3: ML-Based Movies Recommendations View

Lecture 4: Trigger ML Predictions Per User Activity

Lecture 5: Position Ranking for Movie Querysets

Lecture 6: Movie Embedding Idx Field and Task

Lecture 7: Movie Dataset Exports

Lecture 8: Schedule for ML Training, ML Inference, Movie IDX Updates, and Exports

Chapter 11: Wrap Up

Lecture 1: Overview of a Neural Network Colab Filtering Model

Lecture 2: Thank you and next steps

Instructors

Justin Mitchel
Coding Entrepreneur & Teacher – 568,000+ Students

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 1 votes
4 stars: 3 votes
5 stars: 10 votes

Frequently Asked Questions

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