Introduction
This repository provides a comprehensive guide to both supervised and unsupervised learning techniques, including detailed explanations of key concepts, algorithms, evaluation metrics, and practical applications. The aim is to offer a clear understanding of these fundamental machine learning paradigms, enabling users to apply them effectively in various real-world scenarios.
Key Concepts
Definition: Learning from labeled data where the model is trained to predict the output based on input data.
Types: Regression and Classification.
Algorithms
Linear Regression: Predicts a continuous outcome based on one or more predictors.
Logistic Regression: Predicts a binary outcome using a logistic function.
Decision Trees: A tree-based model for both regression and classification.
Random Forests: An ensemble method using multiple decision trees for improved accuracy.
Evaluation Metrics
Accuracy: Measure of correct predictions.
Precision, Recall, F1-Score: Metrics for classification performance.
Mean Squared Error (MSE): Metric for regression performance.
Applications
Linear Regression: Predicting house prices, sales trends.
Logistic Regression: Spam detection, disease diagnosis.
Decision Trees & Random Forests: Customer churn prediction, medical diagnosis.
Key Concepts
Definition: Learning from unlabeled data to find hidden patterns or intrinsic structures.
Types: Clustering, Dimensionality Reduction.
Algorithms
K-Means Clustering: Partitioning data into K clusters based on distance to centroids.
Hierarchical Clustering: Building a hierarchy of clusters using agglomerative or divisive methods.
DBSCAN: Density-based clustering that can find arbitrary shaped clusters.
PCA (Principal Component Analysis): Reduces dimensionality while preserving variance.
Evaluation Metrics
Silhouette Score: Measures how similar an object is to its own cluster compared to others.
Davies-Bouldin Index: Lower values indicate better clustering.
Calinski-Harabasz Index: Higher values indicate better clustering.
Applications
Customer Segmentation: Grouping customers based on purchasing behavior.
Image Segmentation: Dividing images into regions with similar properties.
Anomaly Detection: Identifying unusual patterns in data.