Applied AI: Building Recommendation Systems with Python (TTAI2360)

Course Objectives

Learning Objectives

This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities.

Working in a hands-on learning environment, guided by our engaging AI expert you’ll:

• Be able to confidently distinguish between the different types of recommendation systems.
• Master the Pandas library, equipping you to shape and prep data for your recommenders.
• Get hands-on experience building both basic and intricate content-based recommendation systems, enabling you to design systems that truly align with user needs and preferences.
• Master the world of data mining techniques, from clustering to dimensionality reduction. You'll become adept at sifting through data to uncover those key insights.
• Explore both user-based and item-based collaborative filtering, ensuring your recommendations are spot-on.
• Be able to design recommenders, and be able to deploy them into the real world using innovative tools like PineCone.

If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals.

Course Prerequisites

Course Agenda

Please note that this topics, agenda and labs are subject to change to cover the most recent technical trends or tools, and may adjust during live delivery based on audience skill level, interests and participation.

DAY ONE

1. Getting Started with Recommender Systems

• Technical requirements
• What is a recommender system?
• Types of recommender systems
• Hands-on Activity / Lab

2. Manipulating Data with the Pandas Library

• Technical requirements
• Setting up the environment
• The Pandas library
• The Pandas DataFrame
• The Pandas Series
• Lab

3. Building your First Recommender with Pandas

• Technical requirements
• The simple recommender
• The knowledge-based recommender
• Hands-on Activity / Lab

4. Building Content-Based Recommenders

• Technical requirements
• Exporting the clean DataFrame
• Document vectors
• The cosine similarity score
• Plot description-based recommender
• Metadata-based recommender
• Suggestions for improvements
• Hands-on Activity / Lab

DAY TWO

5. Getting Started with Data Mining Techniques

• Problem statement
• Similarity measures
• Clustering
• Dimensionality reduction
• Supervised learning
• Evaluation metrics
• Hands-on Activity / Lab

6. Building Collaborative Filters

• Technical requirements
• The framework
• User-based collaborative filtering
• Item-based collaborative filtering
• Model-based approaches
• Hands-on Activity / Lab

7. Using PineCone

• Technical requirements
• Introduction
• Case study and project
• Hands-on Activity / Lab

DAY THREE or OPTIONAL CONTENT

8. Deploying as a Serverless Component (OPTIONAL)

• Technical requirements
• Introduction
• Deploy as a Serverless Service

9. Generative AI and Its Magic with GPT

• Introduction to GPT and Generative AI
• GPT in Recommendation Systems
• Explore GPT's role in fine-tuning user preferences.
• Lab

10. Ethical AI – Navigating the Grey Areas

• Understanding Ethical Implications in AI
• Grasp the moral complexities in recommendation systems.
• Bias and Fairness in Recommenders
• Dissect potential biases in AI-driven recommendations.
• Lab

11. Job Aids Using Generative AI

• Introduction to AI-Powered Job Aids
• Understand how GPT can aid daily tasks.
• Applications in Data Processing and Analysis
• Learn GPT's role in data analytics enhancements.
• Lab

Course Materials

Setup Made Simple! Learning Experience Platform (LXP) 

All applicable course software, digital courseware files or course notes, labs, data sets and solutions, live coaching support channels and rich extended learning and post training resources are provided for you in our “easy access, no install required” online Learning Experience Platform (LXP), remote lab and content environment. Access periods vary by course. We’ll collaborate with you to ensure your team is set up and ready to go well in advance of the class.