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Dive into a comprehensive 3-hour video course covering essential theoretical topics in machine learning, neural networks, and computer vision. Learn about decision trees, random forests, logistic regression, K-nearest neighbors, support vector machines, Naïve Bayes, clustering techniques, principal component analysis, and various neural network architectures. Explore advanced concepts like YOLO object detection, Mask R-CNN, pose estimation, and object tracking. Benefit from whiteboard animations that simplify complex concepts, making the learning experience engaging and accessible for beginners. Gain a solid foundation in machine learning theory without getting bogged down in complex mathematics, emerging with expert-level knowledge upon completion.