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.
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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.
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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Activities
Read 'Vector Databases in Practice' by Alex Petrov
Show steps
Gain a theoretical understanding of vector databases and their applications in recommendation systems.
View
Database Internals: A Deep Dive into How...
on Amazon
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
Show steps
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
Show steps
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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Show all 12 activities
Follow tutorials on Hugging Face library
Show steps
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
Show steps
Develop your understanding of how recommendation systems work and how to create personalized recommendations for users.
Browse courses on
Personalized Recommendations
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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.
Read 'Vector Databases in Practice' by Alex Petrov
Show steps
Gain a theoretical understanding of vector databases and their applications in recommendation systems.
View
Database Internals: A Deep Dive into How...
on Amazon
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
Show steps
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
Show steps
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.
Follow tutorials on Hugging Face library
Show steps
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
Show steps
Develop your understanding of how recommendation systems work and how to create personalized recommendations for users.
Browse courses on
Personalized Recommendations
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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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