Lecture
Linear Regression & Correlation
Related lectures (32)
Polynomial Regression: Overview
Covers polynomial regression, flexibility impact, and underfitting vs overfitting.
Logistic Regression: Probabilistic Interpretation
Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.
Feature Engineering: Missing Data and Standardization
Covers techniques for handling missing data and standardizing features, as well as transforming input and output data.
Linear Regression: Basics and Applications
Covers the basics of linear regression in machine learning, exploring its applications in predicting outcomes like birth weight and analyzing relationships between variables.
Probabilistic Linear Regression
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Machine Learning Fundamentals: Regularization and Cross-validation
Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.
Supervised Learning Fundamentals
Introduces the fundamentals of supervised learning, including loss functions and probability distributions.
Linear Regression: Basics and Applications
Covers the basics of linear regression, from training to real-world applications and multi-output scenarios.
Introduction to Data Analysis
Introduces data analysis basics, statistical concepts, Python libraries, and real-world applications.
Nonlinear ML Algorithms
Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.