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Free Download Building Recommendation Engine With Machine Learning & Rag
Published: 3/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.72 GB | Duration: 3h 21m
Learn how to build product, movie, music recommendation engines using Tensorflow, Keras, Surprise, SVD, and RAG

What you'll learn
Learn the basic fundamentals of recommendation engine, such as getting to know its use cases, technical limitations, RAG implementation in recommendation system
Learn how to build product recommendation engine using Tensorflow and Keras
Learn how to build movie recommendation engine using Surprise
Learn how to build music recommendation engine using retrieval augmented generation
Learn how to build product recommendation engine using TFIDF Vectorizer and Cosine Similarity
Learn how to build search based recommendation engine using RAG
Learn how recommendation engines work. This section cover, data collection, preprocessing, feature selection, model training, model evaluation, and deployment
Learn how to perform feature selection for product recommendation engine
Learn how to perform feature selection for movie recommendation engine
Learn how to build and train collaborative filtering model
Learn how to load RAG model and create Facebook AI Similarity Search index
Learn how to build user interface for recommendation engine using Gradio and Streamlit
Learn how to test and deploy recommendation engine on Hugging Face
Learn how to download dataset using Kaggle Hub API
Requirements
No previous experience in machine learning is required
Basic knowledge in Python
Description
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. This section cover, data collection, preprocessing, feature selection, model training, model evaluation, and deploymentLearn how to download dataset using Kaggle Hub APILearn how to perform feature selection for product recommendation engineLearn how to build product recommendation engine using Tensorflow and KerasLearn how to build product recommendation engine using TFIDF Vectorizer and Cosine SimilarityLearn how to perform feature selection for movie recommendation engineLearn how to build movie recommendation engine using SurpriseLearn how to build and train collaborative filtering modelLearn how to build music recommendation engine using retrieval augmented generationLearn how to load RAG model and create Facebook AI Similarity Search indexLearn how to build search based recommendation engine using RAGLearn how to build user interface for recommendation engine using Gradio and StreamlitLearn how to test and deploy recommendation engine on Hugging Face
Software engineers who are interested in building recommendation engine using machine learning,Data scientists who are interested in transforming customer data into relevant product recommendations

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https://www.udemy.com/course/building-recommendation-engine-with-machine-learning-rag/

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