Data Preprocessing you own this product

prerequisites
basics of linear algebra • intermediate Python data science libraries • intermediate recommender system experience (specifically Two Towers)
skills learned
parse and engineer features using built-in and external Python libraries • use NLP to prepare the data and design features’ vocabularies for future embeddings
Shaked Zychlinski
1 week · 6-8 hours per week · ADVANCED
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Look inside

In this liveProject, you’ll design a movie recommendation system step by step, from initial development all the way to a production-ready system. You’ll begin with preparing the data, then you’ll analyze the data, and finally, you’ll preprocess and export it. Along the way, you’ll gain a firm understanding of the data and your users, which is key for developing a system that makes appropriate recommendations. While this liveProject doesn’t contain any machine learning, going through these steps will prepare you for feature engineering and hyperparameter tuning later on.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Shaked Zychlinski

Shaked is currently leading the recommendation research group and company’s recommendations efforts at Lightricks, developing the company's RS algorithms from the ground up. Prior to this, he worked at and led projects at the Algo group of Taboola, one of the largest content recommendation companies in the world. He is a featured writer on Towards Data Science, with hundreds of reads each day. He has also developed the Dython library for Python, with 26k (and counting) downloads a month.

prerequisites

This liveProject is for data scientists who are familiar with recommender systems and want to take the next step in their career. To begin these liveProjects you will need to be familiar with the following:


TOOLS:
  • Intermediate Python (NumPy, pandas, Matplotlib)
  • Intermediate scikit-learn
TECHNIQUES
  • Basic linear algebra (vectors, spaces, matrix transformations)
  • Data analysis & EDA
  • Data preprocessing

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