Building Recommendation Engine with Machine Learning & RAG

About this course

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

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