• Classroom
  • Online, Instructor-Led

In today's digital landscape, recommendation systems are the driving force behind many of the personalized experiences we encounter daily. Think of the precision with which platforms like Netflix or Spotify cater content to individual tastes; that's the magic of recommendation systems in action. Our two-day intensive course, Building Recommendation Systems using Python, will immerse you in the captivating world of data-driven personalization.

The journey starts with a solid foundation, acquainting you with the core concepts and the varied types of recommender systems. As you delve deeper, you'll harness the robust capabilities of the Pandas library, a crucial tool for data manipulation, setting the stage for constructing both rudimentary and advanced content-based recommenders. From here, the course ventures into the intricacies of data mining techniques, allowing for a richer understanding and application of recommendation principles.

The core value of this course Lies in its practical approach. Not only will you navigate the theoretical waters, but you'll also embark on a hands-on adventure with PineCone, a groundbreaking tool in the machine learning domain. This ensures a comprehensive learning experience, preparing you to craft and deploy scalable recommendation models adeptly.

Upon completing this course, you’ll be well-versed in the nuances of recommendation systems, empowered with the skills to design, implement, and optimize these systems, priming you to elevate user experiences, boost customer engagement, and drive informed decisions across varied digital platforms.

Learning Objectives

  • 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.

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):