Why recommendation systems? - Python Tutorial

Contents

• Introduction Introduction

  • Why recommendation systems?

    36s
• 1. Data Prep 1. Data Prep

  • Data in recommendation systems

    4m 20s
  • Data splitting

    2m
  • The cold start problem

    3m 38s
  • Coding: Data prep

    6m 7s
• 2. Modeling 2. Modeling

  • Recommendation systems algorithms

    3m
  • Collaborative filtering

    6m 12s
  • Content-based filtering

    4m 33s
  • Building your first collaborative filtering solution

    4m 40s
  • Building your first content-based filtering solution

    3m 37s
  • Evaluation of recommendation systems

    4m 14s
  • Coding: Collaborative filtering algorithm

    3m 59s
  • Coding: Content-based filtering algorithm

    3m 29s
• 3. Deployment 3. Deployment

  • Recommendation system architectures

    4m 53s
  • Evaluation in production

    3m 58s
  • Coding: Batch architecture

    4m 29s
• 4. MLOps 4. MLOps

  • Tests in recommendation systems

    5m 40s
  • The machine learning lifecycle

    4m 43s
  • Coding: Tests with GitHub actions

    3m 40s
• Conclusion Conclusion

  • Continuing on with recommendation systems

    30s