Blogs

Gradient Descent is optimization algorithm of find optimal solution to problems. In Machine Learning it is used to find optimal parameters of model to minimize cost function.

Evaluating Machine Learning performance is essential for selecting right model, tuning parameters, detecting overfitting/underfitting, and assesing the model generalization ability to new data (unseen data in training process).

Perceptron Classifier is one of the classification algorithms in machine learning. It is similar to linear regression model because it uses combination of features to predict.

Machine learning models aim for generalization to new data. Underfitting (too simple) and overfitting (too complex) hinder this. Achieving balance ensures effective, robust performance.

K-Means is a popular clustering algorithm, simplifying data partitioning based on similarity for efficient pattern recognition in diverse applications.