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                                            1. Introduction to Statistics in ML
 Role of statistics in Machine Learning
 Types of data: qualitative vs quantitative
 Descriptive vs inferential statistics

2. Descriptive Statistics
 Measures of central tendency: mean, median, mode
 Measures of dispersion: variance, standard deviation, range
 Percentiles and quartiles
 Visualization: histograms, box plots, scatter plots

3. Probability Theory
 Random variables & probability distributions
 Discrete vs continuous distributions
 Common distributions: Bernoulli, Binomial, Poisson, Normal
 Expectation, variance, and covariance
 Conditional probability & Bayes’ theorem

4. Sampling & Estimation
 Population vs sample
 Sampling techniques
 Point estimates & confidence intervals
 Law of Large Numbers & Central Limit Theorem

5. Hypothesis Testing
 Null and alternative hypotheses
 Type I & Type II errors
 t-test, z-test, chi-square test
 p-values and significance levels

6. Correlation & Regression Basics
 Correlation coefficient (Pearson & Spearman)
 Simple linear regression (concept + interpretation)
 Multiple regression intuition
 Coefficient of determination (R²)

7. Advanced Topics in Applied Statistics
 Maximum Likelihood Estimation (MLE)
 Bayesian statistics basics
 Bias-variance tradeoff
 Statistical thinking for model evaluation

8. Practical Applications
 Statistical analysis of ML datasets
 Feature selection using statistical tests
 Connecting statistical insights to ML model performance

Hands-on exercises using Python (NumPy, Pandas, SciPy)                                    

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