The simulation of human intelligence by machines to perform tasks like reasoning, learning, and decision-making.

The key domains of AI include Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), and Robotics — each focusing on different ways machines learn, understand, and act intelligently.

AI is the broader field of intelligent systems, while ML is a subset focused on learning from data.

A type of machine learning that uses neural networks with multiple layers to model complex data.

The use of AI systems to perform repetitive tasks without human intervention.

A system of algorithms modeled after the human brain to recognize patterns.

A learning approach where agents learn through rewards and penalties.

Learning from labeled data to make predictions on new data.

Finding patterns or clusters in unlabeled data.

Unfair or prejudiced results caused by biased data or model design.

The data used to teach an AI model how to make predictions.

Data used to evaluate how well the AI model performs on unseen examples.

The process of selecting and transforming variables to improve model performance.

The process of teaching an AI model to identify patterns from data.

Testing a model's performance using metrics like accuracy, precision, and recall.

Using a pre-trained model on a new task to reduce training time and data needs.

AI systems that make their predictions understandable to humans.

When an AI model's performance decreases over time due to changing data patterns.

Adjusting a pre-trained model's parameters to fit a specific use case or dataset.

When a model performs well on training data but poorly on new, unseen data.

AI personalizes campaigns, predicts customer behavior, and automates ad optimization.

For diagnosis assistance, drug discovery, and personalized treatment recommendations.

In fraud detection, credit scoring, and algorithmic trading.

Through adaptive learning systems, grading automation, and personalized tutoring.

Through chatbots, virtual assistants, and sentiment analysis tools.

For product recommendations, inventory management, and customer insights.

For predictive maintenance, quality control, and automation.

In route optimization, self-driving systems, and traffic prediction.

For resume screening, employee engagement analysis, and workforce planning.

To detect threats, prevent intrusions, and automate security responses.

AI that creates new content like text, images, or audio from learned patterns.

A generative AI model trained on massive datasets to understand and produce language.

The smallest unit of text (word or subword) that the model processes.

The practice of crafting effective prompts to guide AI model outputs.

A technique combining data retrieval with generation for more accurate AI responses.

Training an LLM further on a domain-specific dataset.

Getting an AI to perform a task without prior examples.

Providing a few examples in the prompt to guide model behavior.

A prompting technique that guides models to explain their reasoning step-by-step.

A system that not only responds but can take actions, make decisions, or use tools autonomously.

The ethical development and deployment of AI ensuring fairness, transparency, and accountability.

The framework of policies and controls managing AI risk and compliance.

The ability to understand and explain how an AI model makes its decisions.

Ensuring AI systems do not discriminate against individuals or groups.

Assigning responsibility for outcomes produced by AI systems.

Protecting user data used in training and operating AI systems.

AI increases productivity and innovation but may disrupt certain jobs.

By enabling data-driven decision-making and intelligent automation.

Providing insights, predictions, and recommendations for smarter business outcomes.

Growth in multimodal AI, agentic systems, and responsible AI adoption in enterprises.

Raw facts and figures that can be processed into meaningful information.

Structured, semi-structured, and unstructured.

Data organized in predefined formats like rows and columns.

Data without a fixed format, such as images, videos, or text.

Data that has some organizational structure but not rigidly defined, like JSON or XML.

A structured collection of data stored and managed electronically.

Data that describes other data (e.g., column names, data types).

The accuracy, completeness, consistency, and reliability of data.

The framework of policies and standards ensuring data integrity, security, and usability.

A document describing data elements, formats, and relationships in a dataset.

The process of examining data to uncover insights, trends, and patterns.

Descriptive, diagnostic, predictive, and prescriptive analytics.

Understanding what happened in the past using historical data.

Analyzing data to determine why something happened.

Using statistical models to forecast future outcomes.

Recommending actions based on data predictions and simulations.

A statistical relationship between two or more variables.

When one variable directly influences another.

A data point significantly different from other observations.

A Key Performance Indicator — a measurable value that shows progress toward a goal.

The graphical representation of data to make insights easier to understand.

A visual display of key metrics and trends in one place.

A visualization showing data points tracked over time.

To show relationships or correlations between two variables.

A chart that uses color to represent data values.

A chart that shows frequency distribution of numeric data.

Combining visuals and narrative to communicate insights effectively.

Metrics are quantitative values; dimensions are attributes that describe them.

A tool to summarize, group, and analyze large datasets.

Clear, accurate, relevant, and easy to interpret.

Structured Query Language — used to manage and query relational databases.

A unique identifier for each record in a table.

A field linking data between two tables.

Organizing database tables to reduce redundancy.

A clause used to combine rows from two or more tables.

Centralized storage for structured business data for analysis and reporting.

Extract, Transform, Load — the process of moving and preparing data for analysis.

A system that automates the movement and transformation of data.

Extremely large datasets requiring specialized tools for storage and processing.

Non-relational databases designed to handle unstructured or semi-structured data.

The use of data tools and dashboards to support decision-making.

Making business decisions based on analysis and facts rather than intuition.

The ability to read, understand, and communicate using data.

A professional who interprets data and creates insights for business improvement.

A professional who builds models and algorithms to extract advanced insights from data.

The process of building systems that collect, store, and prepare data for analysis.

A storage repository that holds raw data in its native format.

A subset of a data warehouse focused on a specific department or business area.

The responsible use of data respecting privacy, fairness, and transparency.

A: Protecting personal or sensitive data from unauthorized access or misuse.

AI helps automate tasks, analyze data, and make smarter decisions to save time and grow revenue.

It boosts efficiency, reduces costs, improves customer experience, and enhances marketing performance.

ChatGPT, Canva AI, HubSpot AI, Zapier, and Notion AI.

Marketing, customer service, operations, finance, and sales.

By automating repetitive tasks like scheduling, data entry, and email replies.

Chatbots, recommendation systems, automated reporting, and smart invoicing.

By analyzing data, predicting outcomes, and recommending best actions.

Using AI tools to handle repetitive tasks without human intervention.

No — many AI tools offer free or low-cost plans suitable for startups.

Identify repetitive or data-heavy tasks that can be automated.

By automating workflows, managing schedules, and prioritizing tasks.

A sequence of automated actions triggered by data or events, often built in tools like Make or Zapier.

By predicting stock levels, automating reorders, and reducing overstock.

It can screen resumes, schedule interviews, and match candidates faster.

Automating bookkeeping, invoicing, and expense tracking with tools like QuickBooks AI.

Tools like Motion or Reclaim.ai optimize meetings and time blocks automatically.

Automatically creating reports, contracts, or proposals using GenAI.

It drafts responses, summarizes threads, and prioritizes important emails.

By scanning and categorizing information automatically with minimal errors.

Make.com, Zapier, Bardeen, and Pabbly Connect.

By automating ad targeting, content creation, and campaign optimization.

Buffer AI, Hootsuite, and Lately.ai for scheduling, captions, and insights.

By analyzing behavior and sending relevant product recommendations.

Using AI to forecast customer needs and optimize marketing strategies.

By writing subject lines, segmenting lists, and optimizing send times.

Using AI to generate blog posts, captions, or website text.

It tests multiple versions, predicts ROI, and allocates budget automatically.

Through dashboards and insights that reveal campaign effectiveness.

Using data to rank prospects by likelihood to convert.

By predicting churn and suggesting re-engagement strategies.

A conversational AI system that answers questions and supports customers 24/7.

By automating FAQs, ticket routing, and sentiment analysis.

Through feedback analysis and sentiment scoring.

For call transcription, appointment booking, and lead follow-ups.

Analyzing text to detect positive, negative, or neutral emotions.

By tailoring recommendations and offers based on user data.

By analyzing heatmaps, conversions, and automating content recommendations.

Using AI to track and optimize the customer's path from awareness to purchase.

By automating repetitive support tasks, freeing up human agents for complex issues.

By automating review responses and analyzing trends.

By automating core processes, improving decisions, and expanding capacity without hiring more staff.

By analyzing past data to predict sales, expenses, and demand.

It tracks competitor performance, pricing, and sentiment online.

By dynamically adjusting prices based on demand and customer behavior.

By generating financial models, market forecasts, and opportunity analysis.

By turning raw data into visual trends and strategic recommendations.

A system that predicts customer needs and automates relationship management.

By identifying anomalies, fraud, and market shifts early.

Through smart assistants, automation, and focus optimization.

Smarter tools, more integrations, and personalized automation for every entrepreneur.