Probability & Statistics Essentials for Machine Learning
Chapter 1: Probability Foundations Revisited
Review of Sample Spaces and Events
Conditional Probability and Independence
Introduction to Random Variables
Expected Value and Variance
Applying Probability Concepts in Python
Chapter 2: Common Probability Distributions
Bernoulli and Binomial Distributions
Normal (Gaussian) Distribution
Properties and Use in Data Modeling
Working with Distributions in SciPy
Hands-on Practical: Simulating and Plotting Distributions
Chapter 3: Descriptive Statistics for Datasets
Measures of Central Tendency: Mean, Median, Mode
Measures of Dispersion: Variance, Standard Deviation, Range
Understanding Skewness and Kurtosis
Percentiles and Quartiles
Distinguishing Correlation from Causation
Visualizing Data Summaries
Calculating Descriptive Stats with Pandas
Practice: Summarizing a Dataset
Chapter 4: Inferential Statistics: Sampling and Estimation
Overview of Sampling Methods
The Central Limit Theorem
Understanding Point Estimates
Confidence Intervals Explained
Calculating Confidence Intervals for Means
Hands-on Practical: Sampling Simulation and Interval Estimation
Chapter 5: Hypothesis Testing for Model Evaluation
Formulating Null and Alternative Hypotheses
Understanding Type I and Type II Errors
Introduction to Chi-Squared Tests
Analysis of Variance (ANOVA) Overview
Performing Hypothesis Tests using Python
Practice: Applying T-tests to Sample Data
Chapter 6: Introduction to Regression Analysis
The Simple Linear Regression Model
Method of Least Squares Estimation
Interpreting Regression Coefficients
Model Evaluation Metrics (R-squared, MSE)
Assumptions of Linear Regression
Overview of Multiple Linear Regression
Building Regression Models with Python
Hands-on Practical: Fitting and Evaluating a Linear Model