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.

Lessons and practices

Lesson 1: Baseline Prediction Using Global Average

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

Lesson 3: Rating Prediction Using Weighted Average and Pearson Similarity

Adding Unique Counts to Data Loader

Influence Ratings through User Similarity

Enhance Rating Predictions

Refine Your Pearson Correlation Algorithm

Predict User Ratings with Confidence

Lesson 4: Improved Prediction Using Adjusted Weighted Average

Compute User's Average Rating

Uncover User Rating Biases

Predicting User Ratings with Precision

Comparing Prediction Approaches

Interested in this course? Learn and practice with Cosmo!

Practice is how you turn knowledge into actual skills.