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Course Description
Machine learning,
in this course we will learn the foundational concepts and practical applications of machine learning. You will explore various types of learning methods including supervised, unsupervised, and reinforcement learning. We'll cover essential algorithms such as linear regression, logistic regression, decision trees, k-nearest neighbors, support vector machines, and clustering techniques like K-means. You will also gain hands-on experience with model training, evaluation, overfitting, cross-validation, and performance metrics. The course emphasizes real-world problem-solving using Python and popular ML libraries like Scikit-learn and TensorFlow. Whether you're aiming to build predictive models, automate tasks, or analyze patterns in data, this course provides a comprehensive and accessible path to mastering machine learning techniques. Nation Innovation
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