Breaking It Down: Machine Learning, Deep Learning, Computer Vision, NLP, and Generative AI Explained

Explore the differences between Machine Learning, Deep Learning, Computer Vision, NLP, and Generative AI. Learn their applications, techniques, and how they shape the AI world.

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Introduction

AI has grown from a niche technology into a vital driver of innovation. Specialized fields like Machine Learning, Deep Learning, and Generative AI enable computers to perform tasks such as analyzing images, processing language, and even generating creative content. Let’s explore these fields in detail.

What is Machine Learning (ML)?

Definition:

Machine Learning is a subset of AI that focuses on building algorithms that allow computers to learn from and make decisions or predictions based on data without being explicitly programmed.

Characteristics:

• Involves algorithms like supervised learning, unsupervised learning, and reinforcement learning.
• Relies on structured data (e.g., tables, numerical features).
• Examples of algorithms: Decision Trees, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Neural Networks.