How To Build Recommender System With Machine Learning And Deep Learning?

Building Recommender Systems using different approaches : Deep Learning and Machine Learning?

The most requested application in machine learning and deep learning in Berlin? There are numerous e-commerce companies are based in Berlin, there are numerous job opening to hire data scientists to build a recommender system for their platform?

Learn how to build recommender systems from our trainer from London. Stylianos Kampakis spent over eight years at teaching, training coaching Data Science, Machine Learning and Deep Learning.

Automated recommendations are everywhere – on Netflix, Youtube, Zalando app and on Amazon. Machine Learning Algorithms learn about your unique interests and show the best products or content for you as an individual.

However, do you actually know how does the recommender system work? Do you know there are several ways to build a recommender system? Do you want to learn all of them? Recommender systems are complex, but it is for sure to be able to start in 1-2 days.

Learn a hands-on; you’ll develop your own framework for evaluating and combining many different recommendation algorithms together, and you’ll even build your own neural networks using Tensorflow to generate recommendations from real-world cases.

We’ll cover:

  • Building a recommendation engine
  • Evaluating recommender systems
  • Content-based filtering using item attributes
  • Neighborhood-based collaborative filtering with user-based, item-based, and KNN CF
  • Model-based methods including matrix factorization and SVD
  • Applying deep learning, AI, and artificial neural networks to recommendations
  • Real-world challenges and solutions with recommender systems
  • Case studies
  • Building hybrid, ensemble recommenders

Who should join? 

  • Software developers interested in applying machine learning and deep learning to product or content recommendations
  • Engineers working at, or interested in working at large e-commerce or web companies
  • Computer Scientists interested in the latest recommender system theory and research