**Chapter 1 – Breaking Down Statistics & Probability**: Get to know what statistics & probability mean, their interconnection, and importance in data analysis and AI.**Chapter 2 – Descriptive Statistics**: Dive into the basics of descriptive statistics, covering Mean, Median, Mode, Range, and Variance.**Chapter 3 – Data Distributions**: Understand different types of data distributions including Normal, Binomial and Poisson distributions.**Chapter 4 – Correlation and Covariance**: Learn about the relationship and co-dependence between variables.**Chapter 5 & 6 – Probability Basics**: Explore the fundamentals, rules of probability, and important concepts like dependent and independent events.**Chapter 7 – Conditional Probability**: Learn about conditional probability and its importance in statistical inference.**Chapter 8 – Bayes’ Theorem**: Get introduced to Bayes’ Theorem – a principle at the heart of Machine Learning and AI.**Chapter 9 & 10 – Hypothesis Testing**: Understand the process of hypothesis testing with concepts like Null Hypothesis, P-Value, and Confidence Intervals.**Chapter 11 – Regression Analysis**: Dive deep into regression analysis and understand its crucial role in predictive modeling.**Chapter 12 – Introduction to Predictive Modeling**: Learn the foundations of predictive modeling, concept of training and testing data in ML models.**Chapter 13 – Applications of Statistics & Probability in real-life problems**: Understand how statistics and probability shape decision-making in sectors like healthcare, finance, and of course, AI.

By the end of this course, you’ll be applying the principles of statistics and probability to real-world problems, correlating data, and predicting outcomes. Strap in for a statistically significant ride!