PickAClass · Foundations & Lifelong Academics · Mathematics

Probability & Statistics

Learn to analyze and interpret data, understand randomness, and make predictions. Covers key concepts like probability distributions, hypothesis testing, and regression.

Probability & Statistics

Introduction to Conditional Probability and Bayesian Networks in Python

Setting CPD Values in Bayesian Networks with Python

Bootstrapping and Confidence Intervals for Beginners

Data Preprocessing for Bayesian Networks

Bayesian Regression for Predictive Modeling

Building Bayesian Networks from Descriptive Graphs with Python

Constructing Confidence Intervals with the Pennies Sample

Confidence Intervals for the Median with Bootstrapping

Bayesian Networks with Simulated Data in Python

Calibration of Predicted Probabilities in Machine Learning

Bayesian Methods in Software Engineering

Understanding Confidence Intervals in Statistics

Handling Outliers in Data Analysis

Building Complex Bayesian Networks and Evaluating Model Accuracy

Model Evaluation with Predicted Probability and Decile Charts

Reading System Random Data in Go

Foundational Statistics and Probability for Bayesian Optimization

Foundations of Bayesian Networks: Key Concepts and Evaluation Methods

Confidence Intervals and Bootstrapping in Python

Rare Event Prediction and Statistical Modeling for Imbalanced Data

Probability Fundamentals for Data Analysis

Bayesian Probability and Decision Making Under Uncertainty

Everyday Statistics for Practical Decision Making

Introduction to Probabilistic Prediction