Build a solid base in statistics to analyze data and power AI & Machine Learning models.
Sub Category
- Data Science
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Objectives
- Grasp the core concepts of statistics that form the backbone of Artificial Intelligence and Machine Learning.
- Understand and apply measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation).
- Interpret and analyze data using quartiles, percentiles, and interquartile range (IQR) to detect outliers.
- Build intuition about probability, random variables, and real-world uncertainty.
- Apply conditional probability and Bayes’ theorem to everyday AI examples such as spam detection, recommendations, and risk prediction.
- Use data visualizations (histograms, boxplots, scatter plots) to uncover patterns and relationships.
- Connect every statistical concept directly to AI and ML workflows — from data cleaning and preprocessing to model evaluation.
Pre Requisites
- No prior statistics or programming knowledge required.
- A basic curiosity about how AI and data work together.
- A willingness to think analytically and follow step-by-step visual explanations.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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