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Bayesian Machine Learning in Python: A/B Testing

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More
4.6
4.6/5
(4,831 reviews)
27,344 students
Created by

9.3

CourseMarks Score®

10.0

Freshness

9.3

Feedback

8.0

Content

Platform: Udemy
Video: 10h 19m
Language: English
Next start: On Demand

Top A/B Testing courses:

Detailed Analysis

CourseMarks Score®

9.3 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

10.0 / 10
This course was last updated on 10/2021 .

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

9.3 / 10
We analyzed factors such as the rating (4.6/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

8.0 / 10
Video Score: 9.1 / 10
The course includes 10h 19m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 2 hours 12 minutes of 17 A/B Testing courses on Udemy.
Detail Score: 9.4 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 5.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
0 resource.
0 exercise.
0 test.

Table of contents

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!

“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 scratch
•Other 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, sets
•Numpy, Scipy, Matplotlib

WHAT 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)

You will 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

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

How much does the Bayesian Machine Learning in Python: A/B Testing course cost? Is it worth it?

The course costs $14.99. And currently there is a 83% discount on the original price of the course, which was $89.99. So you save $75 if you enroll the course now.
The average price is $13.5 of 17 A/B Testing courses on Udemy.

Does the Bayesian Machine Learning in Python: A/B Testing course have a money back guarantee or refund policy?

YES, Bayesian Machine Learning in Python: A/B Testing has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Bayesian Machine Learning in Python: A/B Testing course, but there is a $75 discount from the original price ($89.99). So the current price is just $14.99.

Who is the instructor? Is Lazy Programmer Inc. a SCAM or a TRUSTED instructor?

Lazy Programmer Inc. has created 29 courses that got 119,482 reviews which are generally positive. Lazy Programmer Inc. has taught 468,493 students and received a 4.6 average review out of 119,482 reviews. Depending on the information available, Lazy Programmer Inc. is a TRUSTED instructor.
Artificial intelligence and machine learning engineer
Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I’ve created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I’ve used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I’ve used MySQL, Postgres, Redis, MongoDB, and more.

9.3

CourseMarks Score®

10.0

Freshness

9.3

Feedback

8.0

Content

Platform: Udemy
Video: 10h 19m
Language: English
Next start: On Demand

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