Machine Learning Basics: Models, Data & Neural Networks

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This presentation offers an accessible introduction to machine learning, exploring its significance, evolution, and key applications. Core concepts like supervised, unsupervised, and reinforcement learning are covered in contrast to traditional programming. It delves into the critical role of quality data, preparation steps, and challenges, followed by an overview of learning algorithms like decision trees and SVMs, and neural networks. Popular tools like TensorFlow and PyTorch for deployment...