Explores the challenges of fitting exponential models to data, including handling non-exponential growth and uncertainty in predictions.
A technical blog post documenting notes and code examples while studying machine learning concepts from 'The Little Learner' textbook.
Discusses the nuanced role of assumptions in statistics, distinguishing between necessary and sufficient conditions, and their impact on interpreting models like linear regression.
Explains the core theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
Explores the connection between the Welch-Satterthwaite t-test and linear regression using the sandwich variance estimator.
A theoretical introduction to Linear Regression models, explaining their use for predicting continuous variables and importance in interpretable fields like lending.
Explains the theory behind Linear Regression, a fundamental machine learning model for predicting continuous numerical values.
Explains the theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
Explains the theory behind linear regression models, a fundamental machine learning technique for predicting continuous numerical values.
Explains the theory behind linear regression models, focusing on interpretability and use cases in fields like lending and medicine.
Explains the theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
A review and tutorial on interpretable machine learning, covering Christoph Molnar's book and providing Python code examples for linear/logistic regression.
A review and tutorial covering Christoph Molnar's book on Interpretable Machine Learning, with Python code examples for linear and logistic regression.
Explains the theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
Explains the theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
A crash course on the theory behind linear regression models, a fundamental machine learning algorithm for predicting numerical values.
Explores the statistical challenges of applying linear mixed models to complex survey data with multi-stage sampling, focusing on weighting issues.
Explains the Normal Equation as an alternative to Gradient Descent for linear regression in JavaScript, including implementation.
A guide to implementing vectorized gradient descent in JavaScript for machine learning, improving efficiency over unvectorized approaches.
A guide to implementing linear regression with gradient descent in JavaScript, using a housing price prediction example.
