AI CERTIFICATIONS
AI CERTIFICATIONS
A/B Testing - Comparing two versions of a model or system to determine which performs better.
Automation - Using technology to perform tasks with minimal human intervention.
Data-Driven Decision Making - Using data analysis and insights rather than intuition alone to guide business decisions.
Deployment - The process of putting a trained machine learning model into production for real-world use.
KPI (Key Performance Indicator) - Measurable values demonstrating how effectively AI initiatives achieve business objectives.
Predictive Analytics - Using data, algorithms, and machine learning to predict future outcomes based on historical data.
ROI (Return on Investment) - A measure of the profitability of an AI investment compared to its cost.
Scalability - The ability of an AI system to handle growing amounts of work or expand capacity.
Use Case - A specific business scenario or application where AI can provide value.
Algorithm - A step-by-step procedure or set of rules designed to solve a specific problem or perform a computation.
Artificial Intelligence (AI) - The simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction.
Deep Learning - A subset of machine learning using neural networks with multiple layers to learn complex patterns from large amounts of data.
Machine Learning (ML) - A subset of AI where algorithms learn patterns from data without being explicitly programmed for every scenario.
Model - The output of a machine learning algorithm trained on data, used to make predictions or decisions.
Test Data - A separate dataset used to evaluate how well a trained model performs on unseen data.
Training Data - The dataset used to teach a machine learning model to make predictions or decisions.
Dataset - A collection of data organized for analysis, typically in rows (observations) and columns (features).
Data Preprocessing - Cleaning and transforming raw data into a format suitable for machine learning models.
Feature - An individual measurable property or characteristic of a phenomenon being observed (e.g., price, age, color).
Feature Engineering - The process of using domain knowledge to create, select, and transform variables that make machine learning algorithms work better.
Feature Extraction - Using algorithms to automatically transform raw data into a reduced set of meaningful features.
Missing Data - Incomplete information in a dataset that must be addressed before training a model.
Normalization - Scaling numerical data to a standard range, typically 0 to 1, to improve model performance.
Outlier - A data point that differs significantly from other observations, potentially affecting model accuracy.
AI Governance - The framework of policies, processes, and controls for responsible AI development and use in an organization.
Bias in AI - Systematic unfairness in AI outcomes, often reflecting biases in training data or design.
Compliance - Adhering to legal and regulatory requirements when implementing AI systems.
Data Privacy - Protecting sensitive information used by AI systems from unauthorized access or misuse.
Ethical AI - Developing and deploying AI systems that are fair, transparent, and aligned with human values.
Explainability - The ability to understand and interpret how an AI model makes its decisions.
Accuracy - The percentage of correct predictions made by a model out of all predictions.
Bias - Systematic error in predictions, often from oversimplified assumptions in the model.
Confusion Matrix - A table showing correct and incorrect predictions broken down by category.
Cross-Validation - A technique that divides data into multiple subsets to better evaluate model performance.
F1 Score - A metric combining precision and recall into a single measure of model performance.
Generalization - A model's ability to perform well on new, unseen data beyond its training set.
Overfitting - When a model learns training data too well, including noise and outliers, resulting in poor performance on new data.
Precision - Of all positive predictions made, the percentage that were actually correct.
Recall - Of all actual positive cases, the percentage the model correctly identified.
Underfitting - When a model is too simple to capture the underlying patterns in the data, performing poorly on both training and test data.
Validation - The process of assessing how well a model performs on data it hasn't seen during training.
Variance - The model's sensitivity to fluctuations in the training data.
Classification - Predicting which category an input belongs to (e.g., spam vs. not spam).
Clustering - Grouping similar data points together without predefined categories.
Computer Vision - AI field focused on enabling computers to interpret and understand visual information from images and videos.
Natural Language Processing (NLP) - AI technology enabling computers to understand, interpret, and generate human language.
Neural Network - A computing system inspired by biological neural networks, composed of interconnected nodes that process information.
Regression - Predicting a continuous numerical value (e.g., sales forecast, price prediction).
Supervised Learning - Machine learning where the model learns from labeled data with known outcomes.
Unsupervised Learning - Machine learning where the model finds patterns in data without predefined categories.
This vocabulary list provides foundational terminology for understanding AI applications in business contexts at an introductory associate degree level.
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