Deep Learning Recommender System

Data Preprocessing you own this product

This free project is part of the liveProject series Real-World Deep Learning Recommender System
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
filed under
try now

placing your order...

Don't refresh or navigate away from the page.
This free project is part of the liveProject series Real-World Deep Learning Recommender System explore series
Check your email for instructions on accessing Data Preprocessing (liveProject)
continue shopping
adding to cart

choose your plan

pro

monthly
annual
$24.99
$249.99
only $20.83 per month
  • share your subscription with one other person
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose another free eBook every time you renew
  • choose twelve free eBooks per year
  • exclusive 50% discount on all purchases
  • Data Preprocessing eBook for free

team

monthly
annual
$49.99
$499.99
only $41.67 per month
  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose another free eBook every time you renew
  • choose twelve free eBooks per year
  • exclusive 50% discount on all purchases
  • Data Preprocessing eBook for free
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.

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

you will learn

In this liveProject, you’ll learn to clean and parse data, analyze the dataset, and translate free-text to pre-trained embeddings using NLP models:


  • Read, clean, fix and join datasets using pandas
  • Parse and engineer features using built-in and external Python libraries
  • Use NLP to prepare the data and design features’ vocabularies for future embeddings

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
RECENTLY VIEWED