1.ML
AI & Machine Learning

Foundational AI algorithm that predicts continuous values by finding optimal linear relationships between features and targets using least squares optimization.

Powerful ensemble AI method combining multiple decision trees with bagging and feature randomization for robust predictions and feature importance ranking.

Core artificial intelligence architecture with interconnected layers of neurons that learn complex patterns through backpropagation and gradient descent optimization.

Revolutionary AI architecture using self-attention mechanisms, powering GPT, BERT, and modern Large Language Models (LLMs) for NLP and beyond.

Classic unsupervised AI algorithm that partitions data into K clusters by iteratively minimizing within-cluster variance using centroid-based optimization.

Essential dimensionality reduction technique using eigendecomposition to transform high-dimensional data into principal components of maximum variance.

Deep learning neural networks that learn compressed data representations, used for anomaly detection, denoising, and generative AI applications.

Generative Adversarial Networks - revolutionary AI architecture with generator and discriminator networks competing to create realistic synthetic data and images.

Model-free reinforcement learning AI algorithm that learns optimal action-value functions through exploration-exploitation balancing and temporal difference updates.

Advanced reinforcement learning method that directly optimizes policy neural networks to maximize expected cumulative rewards using gradient ascent.

Proximal Policy Optimization - state-of-the-art reinforcement learning algorithm used to train ChatGPT and AI agents with stable, efficient policy updates.

Convolutional Neural Networks - specialized deep learning architecture for computer vision, image recognition, and visual AI with learnable filters and pooling layers.