Master Recommendation Systems: From Product to Music with ML & RAG

What you will learn:

Build product recommendation engines using TensorFlow and Keras
Build movie recommendation engines using Surprise
Build music recommendation engines using Retrieval Augmented Generation (RAG)
Master collaborative filtering techniques
Implement feature selection strategies for optimal performance
Utilize TFIDF Vectorizer and Cosine Similarity for recommendation systems
Build search-based recommendation engines using RAG
Create user-friendly interfaces using Gradio and Streamlit
Deploy recommendation engines to Hugging Face Space
Learn the fundamentals of recommendation engines, including use cases, limitations, and RAG integration
Efficiently acquire datasets using the Kaggle API
Master the complete workflow: from data collection to deployment

Description

Dive into the world of recommendation systems with our comprehensive course! Learn to build powerful engines for products, movies, and music using TensorFlow, Keras, Surprise, SVD, and Retrieval Augmented Generation (RAG). This project-based course goes beyond theory, guiding you through every step – from data collection and preprocessing to model training, evaluation, and deployment on Hugging Face Space.

We'll start with the fundamentals of recommendation engines, exploring their applications, limitations, and the power of RAG for enhancing system accuracy. Then, we'll tackle practical projects: building a product recommender with TensorFlow and Keras, a movie recommender using Surprise’s collaborative filtering, and a sophisticated music recommender leveraging RAG's ability to incorporate real-time information. You'll master feature selection, model building, and user interface creation with Gradio and Streamlit. By the end, you'll have not just the theoretical knowledge but the practical skills to build and deploy your own high-performing recommendation systems.

Why learn this? Recommendation systems are crucial for businesses to improve customer engagement, boost sales, and enhance the user experience. This course equips you with the skills to build this critical component of modern applications.

What you'll learn: Data acquisition (Kaggle API), data preprocessing, TensorFlow, Keras, collaborative filtering (Surprise), RAG implementation, feature selection strategies, model training and evaluation, building user interfaces (Gradio, Streamlit), and deployment on Hugging Face Space.

Curriculum

Course Introduction & Setup

This introductory section lays the groundwork for the course. You will get an overview of the course structure ('Introduction' and 'Table of Contents'), understand the target audience ('Whom This Course is Intended for?'), and learn about the necessary tools, IDE, and datasets required ('Tools, IDE, and Datasets').

Understanding Recommendation Engines

Gain a solid grasp of the fundamentals of recommendation engines ('Introduction to Recommendation Engine'), and learn the step-by-step process of building one ('How Recommendation Engine Works?'). Efficiently discover and download datasets from Kaggle using the provided API ('Finding & Downloading Datasets From Kaggle').

Product Recommendation with TensorFlow & Keras

This section focuses on building a product recommendation engine. It begins with crucial feature selection ('Features Selection for Product Recommendation Engine'), followed by building and training the engine using TensorFlow and Keras ('Building & Training Product Recommendation Engine'). You'll then develop the functionality for generating product recommendations ('Creating Function to Generate Product Recommendations'). An alternate approach using TFIDF Vectorizer and Cosine Similarity is also explored ('Building Product Recommendation Engine with TFIDF Vectorizer & Cosine Similarity').

Movie Recommendation with Surprise

Learn to create a movie recommendation engine using the Surprise library. This involves selecting appropriate features ('Features Selection for Movie Recommendation Engine'), building and training a collaborative filtering model ('Building & Training Collaborative Filtering Model'), generating recommendations ('Creating Function to Generate Movie Recommendation'), and building a user-friendly interface using Gradio ('Building User Interface for Movie Recommendation Engine with Gradio').

Music Recommendation with RAG

This section dives into building an advanced music recommendation engine using Retrieval Augmented Generation (RAG). You'll learn to load a pre-trained RAG model, create a Facebook AI Similarity Search index ('Loading RAG Model & Creating Facebook AI Similarity Search Index'), generate music recommendations ('Creating Function to Generate Music Recommendation'), and construct a user interface with Gradio ('Building User Interface for Music Recommendation Engine with Gradio'). We'll also build a search-based music recommender using RAG ('Building Search Based Music Recommendation Engine with RAG').

Deployment and Conclusion

Finally, learn to rigorously test your recommendation engine and deploy it to Hugging Face Space for easy access and sharing ('Testing & Deploying Recommendation Engine on Hugging Face'). The course concludes with a comprehensive summary ('Conclusion & Summary').