Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.
Focuses on advanced pandas functions for data manipulation, exploration, and visualization with Python, emphasizing the importance of understanding and preparing data.
Explores the ROC curve, True Positive Rate, False Positive Rate, and prediction probabilities in classification models.
Covers continuous random variables, probability density functions, and distributions, with practical examples.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Covers the binary choice model, error term assumptions, specific constants, invariances, and distribution properties.
Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.
Covers the fundamentals of Bayesian estimation, focusing on the application of Bayes' Theorem in scalar estimation.
Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.
