Data Preparation (PART 2) - Building a Netflix Recommendation System

#deeplearning #recommendations #ai #data #datascience #machinelearning Recommendation Systems is one of the most crucial and essential skills to have as a Data Scientist. See Full Tutorial Here: https://datamenor.ai Subscribe for more free tutorials : https://www.youtube.com/channel/UC7YlhTyLxk_B1ZcY8CUpCVQ?sub_confirmation=1 In this tutorial, you will build your own Netflix recommendation Sysytem that is capable of effectively recommending movies to users. Note: some students have gone ahead to make this their final year project in Data Science and Machine Learning and received great recognition so I suggest you take it serious and really understand the concepts. You will learn from scratch to advance level the various concepts in recommendation system such as: 1. Recommendation System: An Overview 2. Where Recommender Systems came from 3. Applications of Recommendation Systems 4. Why Recommender Systems? 5. Types of Recommender Systems 6. Popularity based Recommender Systems 7. LAB SESSION: Popularity based Recommender 8. Content-based Filtering: An Overview 9. Cosine Similarity 10. Cosine Similarity with Python 11. Document Term Frequency Matrix 12. LAB SESSION: Building Content-based Recommender Engine 13. Collaborative Filtering: An Introduction 14. Evaluation Metrics for Recommender Systems See Full Tutorial Here: https://datamenor.ai In this tutorial, we will learn everything you need to know to building effective and robust Recommendation Systems. By the end of this tutorial, you will do an awesome project to build your own recommendation system. Subscribe and click notification bell for more videos : https://bit.ly/3xfNeRN RESOURCES: Popularity Based code: https://colab.research.google.com/drive/1c9v3fkl4m14do8yqG-l_ZscQmoI3Kpg_?usp=sharing movies. csv: https://drive.google.com/file/d/1_ed-D9jaIFUimurItNV1nSmb39sBxZ4s/view ratings. csv: https://drive.google.com/file/d/12yJB_ivy-8TMUBtRF_PXQd_dIikpoQ52/view collaborative filtering: https://colab.research.google.com/drive/14v7aWleO6yw3qxgh1XnQ11SlSNnuChY5?usp=sharing text file for document term frequency: https://drive.google.com/file/d/1vOTzRfD2PIqonXICHf-URCO6-Z3XZbQ1/view bank_full dataset: https://drive.google.com/file/d/1VNGDrsINE39Qf0bPf9IbK2VnOMqCYGNC/view lecture slide: https://www.canva.com/design/DAE8kmKAgq8/GFcNFQu3eFVjYAZdHaNELQ/view data 1 processed.ipynb : https://colab.research.google.com/drive/1_lb7NmjF4HOMxnxFulR6pQMuZSk6y0il?usp=sharing data 2 processed.ipynb : https://colab.research.google.com/drive/1_6aiSdaJLfDujklKU1HjyjdVFswaYp1h?usp=sharing data 3 preprocessed : https://colab.research.google.com/drive/1CEbjMZbNl-aff99Z4cLQ8PfR3mNzTKoG?usp=sharing data 4 preprocessed : https://colab.research.google.com/drive/1GNhPYRibRXN7R-k2pEzD6GZmkM5LJErZ?usp=sharing data 5 preprocessed : https://colab.research.google.com/drive/1Jx_SMEvNfFjRDj39YIPKlbVl-rwL_agY?usp=sharing Github link for files : https://github.com/MrBriit/Netflix-Recommender-System-and-Deployment