The Math You Need For ML (Visualized)

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Machine learning (ML) is a subfield of artificial intelligence (AI) that focuses on developing algorithms and statistical models to enable computers to learn and make predictions or decisions without being explicitly programmed. Key techniques in ML include supervised learning, where algorithms are trained on labeled data; unsupervised learning, which involves discovering hidden patterns in unlabeled data; and reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Essential concepts include neural networks, deep learning, feature engineering, model evaluation, and hyperparameter tuning. Applications of ML span various domains, such as natural language processing (NLP), computer vision, predictive analytics, and autonomous systems. ML models are trained using large datasets and employ methods like cross-validation, regularization, and optimization to improve accuracy and generalization.

[@kimjong-un4521]

wow, so simple.

[@abhishekmbc333]

at 3:50 , why do you say the answer is x=3? The question was for what values of x is the slope positive and its positive for all x > 0. So the answer should have been x > 0, thats it. Am I missing something? Please correct me.