Support Vector Regression: Kernel Tricks

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Regression: High Dimensions

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Linear Regression: Foundations and Applications

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Supervised Learning: Likelihood Maximization

Covers supervised learning through likelihood maximization to find optimal parameters.

Linear Regression: Fundamentals and Applications

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Overfitting, Cross-validation & Regularization

Explores model complexity, overfitting, and the role of cross-validation and regularization in machine learning.

Understanding Data Attributes

Covers the analysis of various data attributes and linear regression models.

Feature Engineering: Missing Data and Standardization

Covers techniques for handling missing data and standardizing features, as well as transforming input and output data.