Building a Movie Recommendation Engine | Machine Learning Projects

Building a Movie Recommendation Engine session is part of Machine Learning Career Track at Code Heroku. If you would like to get enrolled in the program you can reach out to us on WhatsApp +91-9967578720

Recommendation Web App Demo: http://www.codeheroku.com/static/movi...

Part 2: Collaborative Filtering:    • Movie Recommendation System with Coll...  

How to build web app for your ML project:
Part 1:   / how-to-turn-your-machine-learning-scripts-...  
Part 2:   / part-2-how-to-turn-your-machine-learning-s...  

All completed Python scripts and associated datasets are on the class Github repo: https://github.com/codeheroku/Introdu...

Alternative Link:
https://drive.google.com/file/d/1sJ9N...

Watch all our Machine Learning videos in the playlist here:
   • Machine Learning  

Prerequisites for this workshop can be downloaded by following instructions here:
http://www.codeheroku.com/post?name=P...

At some point each one of us must have wondered where all the recommendations that Netflix, Amazon, Google give us, come from. We often rate products on the internet and all the preferences we express and data we share (explicitly or not), are used by recommender systems to generate, in fact, recommendations.

In this hands-on workshop we will understand basics of a recommendation system and also build our own. We will be building a content based recommendation engine using Python and Scikitlearn. We will cover concepts such as cosine distance, euclidean distance and when to use each of them. Finally, we will use IMDB 5000 movie dataset to build a content based recommendation engine using CountVectorize and Cosine similarity scores between movies.

Who Should Attend?
You are curious about machine learning and data science
You love building things and learning by working on projects
You are looking for a job in data science / data analytics positions

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