AI & Machine Learning Made Simple Coding 8: KNN Algorithm w Python on free Google Colab!

Point of this series of videos is to make AI & Machine Learning easy to understand via live coding.

For this one I used free web-based Google Colab! No more Jupyter notebook on local machine!

Here are 8 summary points from the provided video transcript with timestamps:

[0:01] Introduction to Google Colab: The speaker introduces Google Colab as a free online platform for creating Jupyter notebooks, eliminating the need to install software on your computer.

[0:52] Demonstrating KNN Algorithm: The speaker plans to demonstrate the K-Nearest Neighbors (KNN) algorithm using Google Colab and mentions it's a repeat of a previous video using Jupyter Notebook.

[1:23] Uploading Files: Explains how to upload files from your computer to Google Colab by using Google Colab's library and provides instructions for the process.

[2:12] Brief Explanation of KNN Algorithm: A brief explanation of the KNN algorithm is given, emphasizing the classification of data points based on the k nearest neighbors.

[3:18] Data Import: The speaker imports data related to breast cancer from an open-source library for use in the KNN algorithm, distinguishing between features (X) and target (Y).

[3:45] Model Training: Discusses training the KNN model using 80% of the dataset while reserving 20% for testing, ensuring reproducibility with the "random state" parameter.

[4:27] Data Scaling: Explains the concept of data scaling or normalization and demonstrates how it is done using an available library.

[6:02] Interpretation of Metrics: The speaker introduces confusion metrics to evaluate the model's performance, emphasizing that the choice of metrics depends on the specific application.