Learn how to build a movie recommendation system using Python. This course will guide you through the entire process, from data preprocessing to deploying your model.
Course Overview
- Introduction to Movie Recommendation Systems
- Data Preprocessing
- Data Cleaning
- Feature Engineering
- Model Building
- Collaborative Filtering
- Content-Based Filtering
- Evaluation and Optimization
- Model Evaluation Metrics
- Hyperparameter Tuning
- Deployment
Learning Outcomes
- Understand the basics of movie recommendation systems
- Implement collaborative filtering and content-based filtering models
- Evaluate and optimize your models for better recommendations
Course Content
Introduction to Movie Recommendation Systems
A movie recommendation system is a system that provides suggestions of movies that a user might like based on their past behavior. This can be achieved through various techniques, including collaborative filtering and content-based filtering.
Data Preprocessing
Data preprocessing is a crucial step in building a recommendation system. It involves cleaning the data and creating meaningful features that can be used by the model.
- Data Cleaning
- Handle missing values
- Remove outliers
- Feature Engineering
- Extract movie features from metadata
- Create user features based on user ratings
Model Building
In this section, you will learn to build both collaborative filtering and content-based filtering models.
- Collaborative Filtering
- User-based collaborative filtering
- Item-based collaborative filtering
- Content-Based Filtering
- Use movie metadata to generate features
- Calculate similarity between movies
Evaluation and Optimization
After building the models, it is important to evaluate their performance and optimize them for better recommendations.
- Model Evaluation Metrics
- Precision, recall, and F1-score
- Mean Average Precision at K (MAP@K)
- Hyperparameter Tuning
- Grid search
- Random search
Deployment
Finally, you will learn how to deploy your movie recommendation system.
- API Development
- Create an API for your model
- Test the API
- Deployment Strategies
- Cloud deployment
- Containerization
Further Reading
For more in-depth learning, check out our advanced course on Advanced Machine Learning.