This glossary provides definitions for key terms you’ll encounter throughout the Courses by Ghazwan’s “AI-Powered Business” courses.

Artificial Intelligence (AI): The simulation of human intelligence processes by machines, particularly computer systems. AI systems are designed to learn from data, adapt to new information, and perform tasks that typically require human intelligence.

Machine Learning (ML): A subfield of AI that allows computers to learn without explicit programming. ML algorithms learn from data to identify patterns and make predictions.

Deep Learning: A type of machine learning inspired by the structure and function of the human brain. Deep learning models use artificial neural networks with multiple layers to process complex data.

Natural Language Processing (NLP): A branch of AI concerned with the interaction between computers and human language. NLP tasks include machine translation, speech recognition, and sentiment analysis.

Computer Vision: An AI field that enables computers to interpret and understand visual information from digital images and videos. Applications include object recognition, facial recognition, and scene understanding.

Robotics: A branch of engineering concerned with the design, construction, operation, and application of robots. AI plays an increasingly important role in robotics, enabling robots to perform tasks with greater autonomy and intelligence.

Algorithm: A set of instructions that a computer follows to perform a specific task. In AI, algorithms are used to train machine learning models and enable them to learn from data.

Data: The raw information used to train and operate AI systems. Data can be structured (e.g., tables) or unstructured (e.g., text, images).

Big Data: Large and complex datasets that are difficult to process using traditional methods. Big data is often used to train AI models.

Bias: Prejudice or preconceptions that can be reflected in AI algorithms and datasets, leading to unfair or inaccurate results.

Explainability: The ability to understand how an AI model arrives at a decision. This is crucial for ensuring trust and transparency in AI applications.

Artificial Neural Network (ANN): A computational model inspired by the structure of the human brain. ANNs consist of interconnected nodes that process information in layers.

Deep Neural Network (DNN): A type of ANN with multiple hidden layers, allowing for more complex learning and pattern recognition.

Machine Learning Model: A software application trained on data to perform a specific task, such as classification, prediction, or recommendation.

Supervised Learning: A type of machine learning where the training data is labeled with the desired output.

Unsupervised Learning: A type of machine learning where the training data is unlabeled, and the model must identify patterns and structures on its own.

Reinforcement Learning: A type of machine learning where the model learns through trial and error, receiving rewards for desired behavior.

Internet of Things (IoT): A network of physical devices embedded with sensors, software, and other technologies that collect and exchange data. AI can be used to analyze data from IoT devices and gain insights for various applications.

This glossary provides a starting point for your understanding of AI terminology. As you progress through the course, you’ll encounter more specific terms related to different AI applications and techniques.