Building Recommendation Engine with Machine Learning & RAG

Welcome to Building Recommendation Engine with Machine Learning & RAG course. This is a comprehensive project based course where you will learn how to build intelligent recommendation systems using Tensorflow, Surprise and Retrieval Augmented Generation. This course is a perfect combination between Python and machine learning, making it an ideal opportunity to level up your programming skills while improving your technical knowledge in software development.

In the introduction session, you will learn the basic fundamentals of recommendation engine, such as getting to know its use cases, technical limitations, and also learn how retrieval augmented generation can be used to improve your recommendation system. Then, in the next section, you will learn step by step how a recommendation engine works. This section covers data collection, data preprocessing, feature selection, model selection, model training, model evaluation, deployment, monitoring, and maintenance.

Afterward, you will also learn how to find and download datasets from Kaggle, it is a platform that offers many high quality datasets from various industries. Once everything is ready, we will start the project. Firstly, we are going to build a product recommendation engine using TensorFlow, it will have the capability of suggesting relevant products to users based on their browsing and purchase history.

This recommendation engine will be able to analyze user behavior, extract meaningful patterns, and generate personalized product recommendations in real time. By implementing this system, businesses can enhance customer engagement, increase conversion rates, and optimize the shopping experience through intelligent suggestions. In the next section, we are going to build a movie recommendation engine using Surprise, which will help users discover films they might enjoy based on their past ratings and preferences.

This recommendation engine will utilize collaborative filtering techniques to find similarities between users and movies, delivering highly personalized recommendations. With this approach, we can improve content discovery, keep users engaged, and drive higher retention rates for streaming platforms. Following that, we are also going to build a music recommendation engine using Retrieval Augmented Generation that is able to provide dynamic and context aware song recommendations.

This recommendation engine will be able to enhance traditional recommendation methods by incorporating real-time external knowledge, improving the accuracy and diversity of song suggestions. Lastly, at the end of the course, we will conduct testing to evaluate the performance of our recommendation engines. After ensuring optimal model performance, we will deploy the recommendation system to Hugging Face Space, where users can select a few initial movies as input, allowing the model to process real-time data and generate personalized recommendations based on learned patterns and similarities.

Before getting into the course, we need to ask this question to ourselves, why should we build a recommendation engine using machine learning? Well, here is my answer, by leveraging machine learning, businesses can offer smarter, more personalized recommendations that keep customers engaged, increase sales, and improve loyalty. Meanwhile, from users perspective, they can benefit from a seamless experience, where they receive valuable recommendations effortlessly, saving time and effort in finding what suits their needs.

Below are things that you can expect to learn from this course:Learn the basic fundamentals of recommendation engine, such as getting to know its use cases, technical limitations, and RAG implementation in recommendation systemLearn how recommendation engines work.