Machine Learning Algorithms Overview

▌1. Supervised Learning

Supervised learning algorithms learn from labeled data — input features with corresponding output labels.

- Linear Regression
- Used for predicting continuous numerical values.
- Example: Predicting house prices based on features like size, location.
- Learns the linear relationship between input variables and output.

- Logistic Regression
- Used for binary classification problems.
- Example: Spam detection (spam or not spam).
- Outputs probabilities using a logistic (sigmoid) function.

- Decision Trees
- Used for classification and regression.
- Splits data based on feature values to make predictions.
- Easy to interpret but can overfit if not pruned.

- Random Forest
- An ensemble of decision trees.
- Reduces overfitting by averaging multiple trees.
- Good accuracy and robustness.

- Support Vector Machines (SVM)
- Used for classification tasks.
- Finds the hyperplane that best separates classes with maximum margin.
- Can handle non-linear boundaries with kernel tricks.

- K-Nearest Neighbors (KNN)
- Classification and regression based on proximity to neighbors.
- Simple but computationally expensive on large datasets.

- Gradient Boosting Machines (GBM), XGBoost, LightGBM
- Ensemble methods that build models sequentially to correct previous errors.
- Powerful, widely used for structured/tabular data.

- Neural Networks (Basic)
- Can be used for both regression and classification.
- Consists of layers of interconnected nodes (neurons).
- Basis for deep learning but also useful in simpler forms.

▌2. Unsupervised Learning

Unsupervised algorithms learn patterns from unlabeled data.

- K-Means Clustering
- Groups data into K clusters based on feature similarity.
- Used for customer segmentation, anomaly detection.

- Hierarchical Clustering
- Builds a tree of clusters (dendrogram).
- Useful for understanding data structure.

- Principal Component Analysis (PCA)
- Dimensionality reduction technique.
- Projects data into fewer dimensions while preserving variance.
- Helps in visualization and noise reduction.

- Autoencoders (Neural Networks)
- Learn efficient data encodings.
- Used for anomaly detection and data compression.

▌3. Reinforcement Learning (Brief)

- Learns by interacting with an environment to maximize cumulative reward.
- Used in robotics, game playing (e.g., AlphaGo), recommendation systems.

▌4. Other Important Algorithms and Concepts

- Naive Bayes
- Probabilistic classifier based on Bayes theorem.
- Assumes feature independence.
- Fast and effective for text classification.

- Dimensionality Reduction
- Techniques like t-SNE, UMAP for visualization and noise reduction.

- Deep Learning (Advanced Neural Networks)
- Convolutional Neural Networks (CNN) for images.
- Recurrent Neural Networks (RNN), LSTM for sequence data.

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