Course Description: This course provides an in-depth exploration of supervised machine learning, covering fundamental principles and modern methodologies for building predictive models. Designed for students and professionals who seek a thorough understanding of machine learning from first principles, the course walks through the essential concepts, theory, and practical applications in statistical learning.
Course Structure:
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Introduction to Statistical Learning: Gain foundational knowledge of statistical learning concepts and how they are applied to predictive modeling. Learn about different types of data, the bias-variance tradeoff, and model evaluation metrics essential for supervised learning.
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Linear Regression: Explore one of the simplest and most powerful models in supervised learning. You will learn how to fit a linear model to data, interpret coefficients, and apply the method to both univariate and multivariate cases.
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Classification: Learn about classification techniques, including logistic regression, discriminant analysis, and K-nearest neighbors. This module focuses on differentiating between various classes in a dataset and predicting categorical outcomes.
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Validation and The Bootstrap Methods: Dive into model validation techniques, including cross-validation, and understand the importance of unbiased performance assessment. You will also learn bootstrap resampling methods, useful for estimating the uncertainty of models.
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Linear Model Selection and Regularization: Understand the importance of selecting the right model and applying regularization techniques such as Ridge Regression and Lasso to prevent overfitting. This module helps to optimize models for better generalization to new data.
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Tree-Based Methods: Explore decision trees, bagging, and boosting methods. This section introduces the power of non-linear models for classification and regression tasks and explains how to enhance their performance with ensemble techniques.
Course Outcomes:
By the end of this course, participants will have a solid foundation in supervised machine learning. You will be equipped to implement and tune models for real-world applications while understanding their theoretical underpinnings. Each topic is reinforced with revision videos and hands-on exercises to solidify learning.