Essential Concepts
Supervised Learning
Learn from labeled data. This paradigm is used for tasks like classification (e.g., spam detection) and regression (e.g., predicting house prices).
Explore →Unsupervised Learning
Discover patterns and structure in unlabeled data. Key applications include clustering (e.g., customer segmentation) and dimensionality reduction.
Explore →Reinforcement Learning
Agents learn to make decisions by performing actions in an environment to maximize a reward. Think of training robots or game AI.
Explore →Feature Engineering
The process of transforming raw data into features that better represent the underlying problem to the predictive models, leading to improved accuracy.
Explore →Model Evaluation
Assessing the performance of a machine learning model. Metrics like accuracy, precision, recall, and F1-score help determine how well a model generalizes.
Explore →Overfitting & Underfitting
Understanding and mitigating common issues where a model performs too well on training data (overfitting) or not well enough on new data (underfitting).
Explore →Bias-Variance Tradeoff
A fundamental concept explaining the relationship between a model's error and its complexity, guiding the choice of models that generalize well.
Explore →Deep Learning Basics
An introduction to neural networks and their architectures, forming the backbone of many state-of-the-art AI applications.
Explore →