Machine Learning
Recommendation Systems Foundations
This course introduces foundational algorithms and concepts that form the backbone of recommendation systems. You'll start with simple baseline prediction models and gradually advance to similarity measures and more sophisticated prediction models. Mastering these fundamentals is essential for developing robust and efficient recommendation tools.
Python
4 lessons
17 practices
3 hours
Coding and Data Algorithms
Lessons and practices
Adding a New User for Analysis
Complete the Matrix Prediction Code
Predicting Ratings for Classical Music
Calculating Mean Ratings with Numpy
Calculate Differences for User Ratings
Fix Pearson Correlation Calculations
Calculate User Similarity in Python
Create Pearson Correlation Function
Adding Unique Counts to Data Loader
Influence Ratings through User Similarity
Enhance Rating Predictions
Refine Your Pearson Correlation Algorithm
Predict User Ratings with Confidence
Compute User's Average Rating
Uncover User Rating Biases
Predicting User Ratings with Precision
Comparing Prediction Approaches
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