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via Udemy |
Go to Course: https://www.udemy.com/course/bayesian-machine-learning-in-python-ab-testing/
Certainly! Here is a detailed review and recommendation for the Coursera course on A/B testing and Bayesian machine learning: --- **Course Review and Recommendation: Mastering A/B Testing with Bayesian Machine Learning** If you're interested in understanding how data-driven decisions are made across marketing, retail, online advertising, and more, this course offers an exceptional deep dive into A/B testing, a fundamental technique in data science. What sets this course apart is its focus on both traditional and advanced, Bayesian methods, providing you with a comprehensive understanding of how to rigorously compare options and make statistically sound decisions. **Course Content and Structure:** The course begins with the basics of traditional A/B testing, highlighting its complexities and limitations. It then guides you through exploring how adaptive methods, such as the epsilon-greedy algorithm and UCB1, can improve upon classical approaches by addressing the explore-exploit dilemma—an essential concept in reinforcement learning. A significant highlight is the transition to Bayesian machine learning techniques. The course emphasizes that Bayesian thinking is a paradigm shift in probability, offering powerful tools for data analysis and decision-making. Topics such as Bayesian updating and probabilistic inference are covered in detail, with a clear focus on their practical application to A/B testing scenarios. **Learning Approach and Practicality:** This course is especially valuable for those who want to learn how to implement machine learning algorithms from scratch. Unlike many courses that merely teach you how to plug data into a library, this course emphasizes understanding through code implementation, making it highly practical and engaging. Every line of code is explained thoroughly, and the instructor encourages repeated learning and deeper comprehension. The course also stresses the importance of mathematical rigor, including probability theory and algorithms, which are essential for mastering machine learning techniques. This makes it suitable for learners comfortable with university-level math and eager to deepen their understanding. **Prerequisites and Recommendations:** To get the most out of this course, you should have a solid foundation in probability, basic Python coding, and familiarity with Numpy, Scipy, and Matplotlib. The course recommends reviewing these prerequisites through linked resources, such as the "Machine Learning and AI Prerequisite Roadmap." **Who Should Enroll?** This course is perfect for aspiring data scientists, machine learning enthusiasts, and professionals seeking to enhance their understanding of A/B testing from a Bayesian perspective. If you're tired of superficial tutorials and want to truly understand the mechanics and implementation of algorithms, this course will meet and exceed your expectations. **Final Thoughts:** I highly recommend this course for anyone serious about developing a deep, practical understanding of A/B testing and Bayesian machine learning. Its focus on coding from scratch, combined with rigorous mathematics and real-world applications, provides a rare and valuable learning experience. Whether you're a beginner looking to solidify your fundamentals or an experienced practitioner seeking advanced insights, this course will empower you with the skills needed to make statistically sound decisions and innovate in your field. **In summary:** - **Pros:** Comprehensive, hands-on coding explanations, focus on Bayesian methods, practical implementation, accessible for motivated learners. - **Cons:** Requires a solid mathematical background; may be challenging for absolute beginners. --- **Take this course if you want to truly understand A/B testing and Bayesian machine learning, and be able to confidently apply these techniques in real-world scenarios.**
This course is all about A/B testing.A/B testing is used everywhere. Marketing, retail, newsfeeds, online advertising, and more.A/B testing is all about comparing things.If you're a data scientist, and you want to tell the rest of the company, "logo A is better than logo B", well you can't just say that without proving it using numbers and statistics.Traditional A/B testing has been around for a long time, and it's full of approximations and confusing definitions.In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things.First, we'll see if we can improve on traditional A/B testing with adaptive methods. These all help you solve the explore-exploit dilemma.You'll learn about the epsilon-greedy algorithm, which you may have heard about in the context of reinforcement learning.We'll improve upon the epsilon-greedy algorithm with a similar algorithm called UCB1.Finally, we'll improve on both of those by using a fully Bayesian approach.Why is the Bayesian method interesting to us in machine learning?It's an entirely different way of thinking about probability.It's a paradigm shift.You'll probably need to come back to this course several times before it fully sinks in.It's also powerful, and many machine learning experts often make statements about how they "subscribe to the Bayesian school of thought".In sum - it's going to give us a lot of powerful new tools that we can use in machine learning.The things you'll learn in this course are not only applicable to A/B testing, but rather, we're using A/B testing as a concrete example of how Bayesian techniques can be applied.You'll learn these fundamental tools of the Bayesian method - through the example of A/B testing - and then you'll be able to carry those Bayesian techniques to more advanced machine learning models in the future.See you in class!"If you can't implement it, you don't understand it"Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...Suggested Prerequisites:Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)Python coding: if/else, loops, lists, dicts, setsNumpy, Scipy, MatplotlibWHAT ORDER SHOULD I TAKE YOUR COURSES IN?:Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math - get important details about algorithms that other courses leave out