AI refers to the mechanism of incorporating human intelligence into machines through algorithms. It’s a combination of “Artificial” (something made by humans or non-natural) and “Intelligence” (the ability to understand or think). Focus: AI aims to mimic human behaviour by enabling machines to learn, reason, and self-correct.
Examples: 1. Natural Language Processing (NLP): AI-powered chatbots, language translation, and sentiment analysis.
2. Computer Vision: Facial recognition, object detection, and image classification.
3. Expert Systems: Medical diagnosis, financial planning, and recommendation engines. #AI
Machine Learning (#ML) allows systems (computers) to learn automatically from experiences without explicit programming. It’s a subset of AI. Process: ML algorithms make observations on data, identify patterns, and improve decision-making based on examples.
Examples:
1. Regression: Predicting house prices based on features like area, location, and number of bedrooms.
2. Classification: Spam email detection, disease diagnosis, and sentiment analysis.
3. Clustering: Grouping similar customers for targeted marketing.
Deep learning (DL) is a subfield of ML that uses neural networks (inspired by the human brain) to mimic brain-like behaviour.
Characteristics:
1. Neural Networks: DL algorithms work with large neural networks, often with multiple layers.
2. Self-Administered Prediction: DL models analyze vast datasets and provide outputs autonomously.
Examples:
1. Image Recognition: Convolutional Neural Networks (CNNs) for identifying objects in images.
2. Natural Language Processing: Recurrent Neural Networks (RNNs) for language modeling and translation.
3. Autonomous Vehicles: DL powers self-driving cars by processing sensor data.
