Distance Metrics: Euclidean, Manhattan & Cosine Similarity

1. 1Calculate Euclidean distance between two points using Python's math library
2. 2Apply Manhattan distance formula to compare differences between data points
3. 3Use Cosine Similarity to measure similarity between vectors
4. 4Visualize the differences between these distance metrics using a Python library like Matplotlib
5. 5Implement a simple clustering algorithm using one of these distance metrics

Data scientists and machine learning engineers can benefit from understanding distance metrics to improve their models' performance and make informed decisions

💡 Different distance metrics can significantly impact the results of machine learning models, so choosing the right one is crucial

📊 Understand distance metrics like Euclidean, Manhattan & Cosine Similarity to measure similarity between data points! #MachineLearning #DataScience