Develop a Movie Recommendation System with Python Machine Learning Cosine Similarity

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http://youtube.com/watch?v=Gy8t5XIrISc

In this tutorial we will develop a Movie Recommendation system. We will use the content-based filtering method during this tutorial. Content-based looks at the items that user has consumed then it finds other items similar items and it recommends them. • 💻 Complete Notebook and associated dataset is on Github repo: • https://github.com/hnawaz007/pythonda... • 🔗 KNN Machine Learning video (Euclidean distance): • KNN Machine Learning Algorithm | KNN ... • 🔗 Pandas Tutorial: • Python Pandas Data Science Tutorial (... • • 🔗 Watch all our Machine Learning videos in the playlist here: • • Machine Learning • • At some point you must have wondered where all the recommendations that Netflix, Amazon, Google give us, come from. We often rate products on the internet doing so we we share our preferences and share data. This data is used by recommendation systems to generate recommendations and adds. • In this tutorial we will understand basics of a recommendation system and also build our own. We will be building a content or item based recommendation system using Python and Scikitlearn. • We will cover concepts such as cosine similarity, distance and how to use them. Finally, we will use TMDB movie dataset to build a content based recommendation system using CountVectorize and Cosine similarity scores between movies. • Target audience: • 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 • #RecommendationSystem #MachineLearning #Python • Topics covered in this video: • 0:00 - Intro to recommendation system • 1:28 - Content based filtering method • 3:40 - Import data and libraries • 5:01 - Stemming process • 6:32 - Encode text to matrix with count vectorizer • 8:10 - Cosine Similarity • 9:29 - Define recommend method • 12:13 Recommend movies

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