Course Description
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!
Suggested Prerequisites:
- 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
Tips for success:
- Use the video speed changer! Personally, I like to watch 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!
- Don't get discouraged if you can't solve every exercise right away. Sometimes it'll take hours, days, or maybe weeks!
- Write code yourself, this is an applied course! Don't be a "couch potato".
Lectures
- 15 sections
- 117 lectures
- 13h 12m total length
Reviews
7,322 reviews for this course
Testimonials and Success Stories
H. Z.
“I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!”
Wade J.
“I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.
You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.
Looking forward to your new stuff.”
Kris M.
“Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!
I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.”
Steve M.
“I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression.
Your courses are just what I have been seeking. I am a retired mathematician, statistician and Supply Chain executive from a large Fortune 500 company in Ohio. I also taught mathematics, statistics and operations research courses at a couple of universities in Northern Ohio.
I have taken many courses and have enjoyed the journey, I am not going to be critical of any of the organizations from whom I have taken courses. However, when I read a review about one of your courses in which the student was complaining that one would need a PhD in Mathematics to understand it, I knew this was the course (or series of courses) that I wanted. (Having advanced degrees in mathematics, I knew that it was highly unlikely that a PhD would actually be required.)”
Saurabh W.
“Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.”
David P.
“I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.
And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!
You are an excellent teacher, and a rare breed.
And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.
(I plan to go through many more courses, one by one!)
I know you must be deluged with complaints in spite of the best content around That's just human nature.
Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)”
P. C.
“Hello, Lazy Programmer!
In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.
The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch.
Course by course, I was renewing the basics and the prerequisites. Thus, in several months, after every day studying under your guidance, I was able to gain enough intuitions and practical skills in order to begin progressing in my research. Having a solid background, it was just a pleasure to read all the relevant papers in the field as well as to make all the experiments needed for achieving my goal – creating a high-performance CNN for offline HCCR.
I believe, the professionalism of any teacher can be estimated by the feedback received from their students, and it’s of the utmost importance for me to thank you, Lazy Programmer!
I want you to know, in spite, that we have never actually met and you haven’t taught me privately, I consider you one of my greatest Teachers.
The most important things I have learned from you (some in the hard way, though) beside many exciting modern Deep Learning/AI techniques and algorithms are:
1) If one doesn’t know how to program something, one doesn’t understand it completely.
2) If one is not honest with oneself about one’s prior knowledge, one will never succeed in studying more advanced things.
3) Developing skills in BOTH Math and Programming is what makes one a good student of this major.
I am still studying your courses, and am certain I will ask you more than just a few technical questions regarding their content, but I already would like to say, that I will remember your contribution to my adventure in the Deep Learning field, and consider it as big as one of such great scientists’ as Andrew Ng, Geoffrey Hinton, and my supervisor.
Thank you, Lazy Programmer! 非常感谢您，Lazy 老师！
If you are interested, you can find my first paper’s preprint here:
https://arxiv.org/abs/xxx”
Dima K.
“By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.”
Andres Lopez C.
“This course is exactly what I was looking for. The instructor does an impressive job making students understand they need to work hard in order to learned. The examples are clear, and the explanations of the theory is very interesting.”
Mohammed K.
“Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.”
Tom P.
“I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.
I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.”
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