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IBM Skills Network Team and Richa Arora

The global recommendation engine market is predicted to grow 37% annually through 2030 (Straits Times). The expertise to predict user preferences and drive engagement using AI recommendation system skills has become an essential business need and a highly sought-after skill using vector databases.

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The global recommendation engine market is predicted to grow 37% annually through 2030 (Straits Times). The expertise to predict user preferences and drive engagement using AI recommendation system skills has become an essential business need and a highly sought-after skill using vector databases.

In this IBM mini-course, you’ll create two shareable projects that demonstrate your proficiency and readiness to develop AI-powered recommendation systems. 

You’ll get step-by-step instructions to create a real-life inspired food ordering recommendation system using Chroma DB and Hugging Face models. For your final project, you’ll use Chroma DB or your choice of PostgreSQL, Cassandra, or MongoDB to create a real-life job search recommendation system. This will demonstrate your ability to generate embeddings and implement similarity searches using Hugging Face natural language processing (NLP) algorithms.

Ready to start? Bring your vector, NoSQL, or relational database vector search skills to this course.  If you don't already have these skills, you can attain these skills in other Vector Databases Fundamentals Specialization courses. 

Enroll today in this mini-course to advance your AI career!

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What's inside

Syllabus

AI Recommendation Systems with Vector Databases
You will create a job recommendation system for your final project in this module by applying many of the skills learned throughout this program. In the first lesson, you will learn about Hugging Face, a growing, open-source AI community. You will learn about several of their crowd-sourced NLP models and how to incorporate them into your projects. In lesson two, we provide you with a practice project before the final project. This non-graded project provides you with step-by-step instructions for building a recommendation system similar to the final project. You will complete the second project independently and will be graded by one of your peers. You will also evaluate one of your peer’s projects using a rubric with objective grading criteria. You also have the opportunity to demonstrate your comprehension of the final project by completing a graded quiz.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Strong foundation for beginners in building AI recommendation systems
Teaches best practices to apply NLP models to generate embeddings and implement similarity search for a recommendation system
Uses various popular platforms such as Chroma DB, Hugging Face and NoSQL or relational databases
Final project helps learners apply many of the skills learned
Graded quiz and peer evaluations provide feedback and reinforcement

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Vector Database Projects: AI Recommendation Systems with these activities:
Read 'Vector Databases in Practice' by Alex Petrov
Gain a theoretical understanding of vector databases and their applications in recommendation systems.
Show steps
  • Read Chapter 1: Introduction to Vector Databases.
  • Read Chapter 2: Building a Recommendation System with Vector Databases.
Review your knowledge of machine learning
Ensure you have a solid foundation in machine learning, which is essential for understanding AI recommendation systems.
Browse courses on Machine Learning
Show steps
  • Review your notes from previous machine learning courses or tutorials.
  • Work through practice problems or online quizzes.
Review vector search basics
Review fundamental concepts like vector space models, dimensionality reduction, and distance metrics before starting the course.
Browse courses on Vector Databases
Show steps
  • Read an overview on vector databases and vector search.
  • Practice using a vector database like Milvus or Weaviate.
  • Complete a simple vector search project using Python or another programming language.
Nine other activities
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Follow tutorials on Hugging Face library
Build familiarity with the Hugging Face library and explore its pretrained NLP models before working on the course projects.
Browse courses on Hugging Face
Show steps
  • Walk through the official Hugging Face tutorials.
  • Experiment with different NLP models using the Hugging Face Hub. Explore models for text classification, sentiment analysis, and question answering.
  • Implement a basic text summarization or text generation project using Hugging Face models.
Create a recommendation system for a fictional movie streaming service
Develop your understanding of how recommendation systems work and how to create personalized recommendations for users.
Show steps
  • Choose a dataset of movie ratings or reviews.
  • Implement a recommendation algorithm using a vector database, such as Chroma DB.
  • Evaluate the performance of your recommendation system using metrics such as precision and recall.
Follow the 'Building a Restaurant Recommendation System with Chroma DB' tutorial
Learn how to use Chroma DB to build a recommendation system hands-on.
Show steps
  • Sign up for a Chroma DB account.
  • Follow the steps in the tutorial.
Join a study group or participate in online forums dedicated to vector databases or AI recommendation systems
Connect with peers and exchange ideas to enhance your understanding.
Browse courses on Vector Databases
Show steps
  • Search for existing study groups or forums.
  • Join a group that aligns with your interests.
Build an AI-powered food recommender system
Gain practical experience by building a real-world AI-powered food recommender system during the course.
Browse courses on Vector Search
Show steps
  • Gather a dataset of food items with attributes like cuisines, ingredients, and nutritional information.
  • Create embeddings for food items using a vector database like Milvus or Weaviate.
  • Train a similarity search model using Hugging Face's NLP models.
  • Develop a Python script or web application to implement the food recommender system.
  • Evaluate the system's performance and iterate to improve accuracy.
Compile a list of resources on AI recommendation systems
Expand your knowledge of AI recommendation systems beyond the course materials.
Browse courses on Resources
Show steps
  • Search the web for articles, blog posts, and videos on AI recommendation systems.
  • Organize the resources into a document or spreadsheet.
Attend a workshop on vector databases or AI recommendation systems
Enhance your understanding and skills in vector databases and recommendation systems through hands-on training.
Browse courses on Vector Databases
Show steps
  • Search for upcoming workshops in your area or online.
  • Register for a workshop that aligns with your interests.
Contribute to an open-source library or framework for vector databases or AI recommendation systems
Deepen your understanding of the underlying technologies and contribute to the community.
Browse courses on Open Source
Show steps
  • Identify an open-source project that aligns with your interests.
  • Review the project's documentation and codebase.
  • Submit a pull request with your contributions.
Volunteer at a data science or AI-related event
Contribute to the field and connect with professionals by volunteering at industry events or open-source projects related to AI recommendation systems.
Browse courses on Data Science
Show steps
  • Research local data science or AI meetups and conferences.
  • Identify volunteer opportunities such as staffing registration, assisting speakers, or organizing workshops.
  • Prepare by reviewing event materials and familiarizing yourself with the topics being covered.
  • Attend the event, engage with attendees and speakers, and participate in discussions.

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