Ideas into intelligence.

Artificial intelligence builds systems that perform tasks associated with human intelligence, including perception, reasoning, language generation and decision support.

Machine learning is an approach to AI where a model learns patterns from examples and applies them to new data.

A large language model learns statistical relationships in text, represents text as tokens and predicts useful continuations from context.

An LLM converts input into tokens, processes them through transformer layers and generates output one token at a time.

Supervised, unsupervised, semi-supervised and reinforcement learning address different data and feedback settings.

Overfitting memorizes training examples; underfitting is too simple to capture useful patterns. Data, capacity and regularization help balance both.

Bias comes from overly simple assumptions; variance comes from sensitivity to the training sample. Good validation balances both.

Parameters are learned from data. Hyperparameters are chosen around training, such as depth, batch size and regularization strength.

Measure where values are missing and why, then use a meaningful default, imputation, an indicator or a model that supports missing values.

Use domain limits, robust statistics, IQR, z-scores and visual checks. Investigate causes before removing, capping, transforming or retaining outliers.

A confusion matrix counts true positives, true negatives, false positives and false negatives, revealing which classes a classifier confuses.

Feature selection keeps useful inputs and removes noisy, redundant or unavailable signals using domain knowledge and model-based checks.

Leakage uses information unavailable at prediction time. Split data before fitting transformations and keep future information out of features.

Logistic regression estimates class probabilities from weighted features. A threshold converts probability into a decision.

A random forest combines decision trees trained on varied samples and feature subsets, reducing variance and providing a strong baseline.

Bagging trains models independently and aggregates them. Boosting trains sequentially so later models focus on errors.

Random forests are robust and easy to tune. XGBoost builds regularized trees sequentially and can achieve excellent tabular accuracy with careful tuning.

F1 is the harmonic mean of precision and recall when both false positives and false negatives matter.

ROC–AUC summarizes ranking across classification thresholds. Precision–recall analysis can be more informative for highly imbalanced data.

Accuracy suits balanced classes. Precision matters when false positives cost more; recall matters when missing a positive case costs more.

Cross-validation rotates training and validation folds to provide a more stable estimate for model comparison.

L1, L2 and dropout discourage overly complex models. Their strength is controlled by a hyperparameter.

The learning rate controls how far an optimizer moves parameters after each update. Too high overshoots; too low slows training.

Tuning compares configurations using validation, exploring depth, learning rate, regularization and batch size.

Principal component analysis projects correlated features into fewer directions that explain much of the variance.

An ML pipeline makes preparation, feature creation, training, evaluation and serving repeatable and consistent.

MLOps combines ML, engineering and operations practices for versioning, testing, deployment, monitoring and retraining.

Model drift is a decline in usefulness as incoming data, behavior or input–outcome relationships change.

A typical lifecycle moves from framing and data collection through labeling, features, training, validation, deployment, monitoring and iteration.