Machine Learning Fundamentals: Overfitting and Regularization

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Overfitting in Supervised Learning: Case Studies and Techniques

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Regularization in Machine Learning

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of model complexity and different cross-validation methods.

Polynomial Regression: Basics and Regularization

Covers the basics of polynomial regression and regularization to prevent overfitting.

Data Representation: BoW and Imbalanced Data

Covers overfitting, model selection, validation, cross-validation, regularization, kernel regression, and data representation challenges.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Recommender Systems: Overview and Methods

Explores the evolution of recommenders, collaborative filtering, Netflix Prize, model training, and optimization techniques.

Model Evaluation

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Overfitting: Symptoms and Characteristics

Explores overfitting in polynomial regression, emphasizing the importance of generalization in machine learning and statistics.

Data Representations and Processing

Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.

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