Size: 1.05 GB
Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More
What you’ll learn
- Use adaptive algorithms to improve A/B testing performance
- Understand the difference between Bayesian and frequentist statistics
- Apply Bayesian methods to A/B testing
Requirements
- Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)
- Python coding with the Numpy stack
Description
This course is all about A/B testing.- calculus
- probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)
- Python coding: if/else, loops, lists, dicts, sets
- Numpy, Scipy, Matplotlib
- Watch it at 2x.
- Take handwritten notes. This will drastically increase your ability to retain the information.
- Write down the equations. If you don’t, I guarantee it will just look like gibberish.
- Ask lots of questions on the discussion board. The more the better!
- Realize that most exercises will take you days or weeks to complete.
- Write code yourself, don’t just sit there and look at my code.
- Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)
Who this course is for:
- Students and professionals with a technical background who want to learn Bayesian machine learning techniques to apply to their data science work
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