- 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!