Building AI #1: Build a Movie Recommendation System with Python | Cosine Similarity

📝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 have consumed then it finds other items similar items and it recommends them.

💻 Complete Notebook and associated dataset is on Github repo:
https://github.com/Lubula/Movies-Data-Analysis-and-Recommendation-Project/blob/main/4_Recommendation_System.ipynb

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 share our preferences and share data. This data is used by recommendation systems to generate recommendations.

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.

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

#recommendationsystems #machinelearning #python #datascience #artificialintelligence #deeplearning #ai #tech

Topics covered in this video:
0:00 - Intro to recommendation system
2:02 - Problem statement and building a movie recommendation Algorithm
6:33 - Import data and libraries
10:33 - EDA
16:00 - Data Preprocessing and Data Cleaning
30:49 - Encode text to matrix with count vectorizer & building Model
57:03 - Model Results

Видео Building AI #1: Build a Movie Recommendation System with Python | Cosine Similarity канала Lubula Paulo Chikwekwe