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Machine Learning (ML): A subset of AI where systems learn patterns from data to make predictions or decisions without being explicitly programmed.

Python Programming: A versatile programming language with rich libraries like Pandas, NumPy, and TensorFlow, making it a preferred tool for Data Science and AI development.

Deep Learning: A subset of Machine Learning that uses Artificial Neural Networks (ANNs) to model complex patterns and perform tasks like image recognition and natural language processing.

Artificial Neural Networks (ANN): Computational models inspired by the human brain, consisting of interconnected nodes (neurons) that process data and identify patterns.

Convolutional Neural Networks (CNN): A type of neural network designed for processing image data, excelling in tasks like object detection, classification, and facial recognition.

Recurrent Neural Networks (RNN): Neural networks designed to process sequential data by remembering previous inputs, commonly used in language modeling and time-series analysis.

Generative AI: A field of AI that creates new content, such as text, images, or music, by learning from existing data, enabling creativity in machines.

PYTHON

Python is a powerful, beginner-friendly programming language used for web development, data science, AI, automation, and more.

MACHINE LEARNING

Machine Learning is a subset of AI that enables computers to learn patterns from data and make predictions without explicit programming.

DEEP LEARNING

Deep Learning is a subset of Machine Learning that uses neural networks with many layers to analyze large datasets and solve complex problems, such as image and speech recognition.

STATISTICS

Statistics is the study of collecting, analyzing, interpreting, presenting, and organizing data. It helps make informed decisions based on data insights and is widely used in various fields such as economics, healthcare, engineering etc.

PYTHON

MACHINE LEARNING

DEEP LEARNING

STATISTICS

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