When predicting continuous values, how do you measure success? This guide covers essential regression metrics like MAE, MSE, RMSE, and R-squared to help you evaluate your regression models.
How do you know if your classification model is any good? Explore key metrics like Accuracy, Precision, Recall, F1-Score, ROC Curve, and AUC to evaluate and compare your models effectively.
Learn about two common pitfalls in Machine Learning: overfitting (model too complex) and underfitting (model too simple). Understand how to identify and mitigate these issues for better model generalization.
Get an overview of popular Machine Learning algorithms like Linear Regression, Logistic Regression, Decision Trees, SVMs, K-Means, and Neural Networks. Understand their basic concepts and use cases.
Explore unsupervised learning, where algorithms find hidden patterns in unlabeled data. Learn about clustering, dimensionality reduction, and their applications.
Supervised learning is a cornerstone of ML. This guide explains what it is, how it works with labeled data, common algorithms (like regression and classification), and real-world examples.