Regression Series
Comprehensive exploration of regression analysis, covering key concepts, advanced techniques, and model assumptions for data-driven decision-making.
Linear Regression in R
Comprehensive guide to linear regression in R: model fitting, assumption checking, interpretation, categorical variables, interactions, and advanced techniques for effective data analysis.
Statistical Rethinking 2022 - Categories, Curves & Splines
Explore statistical concepts like categorical variables, contrasts, and splines. Learn Bayesian causal inference and polynomial models for advanced data analysis and modeling techniques.
In-Database Factorised Learning - Professor Dan Olteanu
Explore in-database factorised learning techniques for efficient data analysis and machine learning, covering integration approaches, advantages, and practical applications in relational databases.
Types of Variables - Numerical vs. Categorical, Discrete vs. Continuous, Ordinal vs. Nominal
Comprehensive overview of variable types in statistics, covering numerical and categorical classifications, with explanations of discrete, continuous, ordinal, and nominal variables.
Conditional Distributions of Categorical Variables in Statistics
Explore conditional distributions of categorical variables through examples and charts. Learn to determine associations between variables and interpret data effectively.
Two Way Tables and Marginal Distributions in Statistics
Explore two-way tables and marginal distributions in statistics. Learn to analyze categorical variables, use percentages, and create bar charts for data interpretation.
Prepping Data for Analysis Using R
Learn techniques for preparing data for analysis using R, covering common issues, automated solutions, and practical demonstrations for handling missing values and categorical variables.
Kaggle 30 Days of ML - Day 12 - Missing Values and Categorical Variables
Explore missing values and categorical variables in machine learning, learning practical approaches to handle data imperfections and enhance model performance.
Entity Embeddings for Categorical Variables
Learn to implement entity embeddings for categorical variables using TensorFlow/Keras, with a focus on converting the model to PyTorch. Practical application using Kaggle competition data.