Python Programming Build A Recommendation Engine In Django

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!
Requirements
Experience Python 3.6+ (such as the first 15/16 days from my course 30 Days of Python)
Django 3.2+ experience (such as my course Your First Django Web Project or any of the Try Django series)
Celery experience is a plus! (Such as my Time & Tasks 2 course)
Machine learning experience is a plus but not required (checkout my Hello World of Machine Learning Course)
Pandas basics is a plus but not required (checkout my Try Pandas Course)
Description
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 ExperiencePython 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)
Overview
Section 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
Section 2: Handling the Dataset
Lecture 6 Generate Fake User Data
Lecture 7 Django Management Command to add Fake User Data
Lecture 8 Our Collaborative Filtering Dataset
Lecture 9 Load The Movies Dataset into the Movie Django Model
Lecture 10 Create Ratings Model with Generic Foreign Keys
Section 3: Running Calculations with Django
Lecture 11 Calculate Average Ratings
Lecture 12 Generate Movie Ratings
Lecture 13 Handling Duplicate Ratings with Signals
Lecture 14 Calculate Movie Average Rating Task
Section 4: Python Celery
Lecture 15 Setup Celery for Offloading Tasks
Lecture 16 Converting Functions into Celery Tasks
Section 5: Django Views & Auth
Lecture 17 Movie List & Detail View, URLs and Templates
Lecture 18 Django AllAuth
Lecture 19 Update the Movie Ratings Task
Section 6: User Ratings
Lecture 20 Rendering Rating Choices
Lecture 21 Display a User's Ratings
Section 7: Dynamic Django with HTMX
Lecture 22 Dynamic Requests with HTMX
Lecture 23 Rate Movies Dynamically with HTMX
Lecture 24 Infinite Rating Flow with Django & HTMX
Lecture 25 Rating Dataset Exports Model & Task
Section 8: Jupyter Notebooks with Django
Lecture 26 Using Jupyter with Django
Lecture 27 Load Real Ratings to Fake Users
Lecture 28 Update Movie Data
Lecture 29 Recommendations by Popularity
Section 9: Machine Learning & Collaborative Filtering
Lecture 30 What is Collaborative Filtering
Lecture 31 Collaborative Filtering with Surprise ML
Lecture 32 Surprise ML Utils & Celery Task For Surprise Model Training
Lecture 33 Batch User Prediction Task
Section 10: Handling Predictions in Django
Lecture 34 Storing Predictions in our Suggestion Model
Lecture 35 Updating Batch Predictions Based on Previous Suggestions
Lecture 36 ML-Based Movies Recommendations View
Lecture 37 Trigger ML Predictions Per User Activity
Lecture 38 Position Ranking for Movie Querysets
Lecture 39 Movie Embedding Idx Field and Task
Lecture 40 Movie Dataset Exports
Lecture 41 Schedule for ML Training, ML Inference, Movie IDX Updates, and Exports
Section 11: Wrap Up
Lecture 42 Overview of a Neural Network Colab Filtering Model
Lecture 43 Thank you and next steps
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.


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