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Bayesian Machine Learning in Python: A/B Testing
BY
Udemy
Develop data science, machine learning, and data analytics skills, and learn to apply them to your marketing, digital ...Read more
Online
₹ 699 4099
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
Bayesian Machine Learning in Python: A/B Testing online certification is created by Lazy Programmer Inc. - Artificial intelligence and machine learning engineer and offered by Udemy Inc., an online learning platform based in the United States that helps individuals learn new skills and professionals refine their existing ones.
With Bayesian Machine Learning in Python: A/B Testing online training, methods that candidates will learn will not be limited to A/B testing; rather, they will use A/B testing as an introduction to how Bayesian techniques can be used. Candidates will master the essential tools of the Bayesian method - using A/B testing as an analogy - and then apply those Bayesian methods to more advanced machine learning models in the future.
Individuals taking the Bayesian Machine Learning in Python: A/B Testing online course, are advised to have prior knowledge of the following topics: probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF), python coding, Numpy, Scipy and Matplotlib so that their lack of knowledge of any particular topic won’t affect their overall learning experience.
The highlights
- Certificate of completion
- Self-paced course
- English videos with multi-language subtitles
- 10.5 hours of pre-recorded video content
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and TV
Program offerings
- Certificate of completion
- Self-paced course
- English videos with multi-language subtitles
- 10.5 hours of pre-recorded video content
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
₹ 699 ₹4,099
certificate availability
Yes
certificate providing authority
Udemy
Who it is for
What you will learn
Machine learning
Data science knowledge
Knowledge of python
After completing the Bayesian Machine Learning in Python: A/B Testing certification course, candidates will get an understanding of how python and machine learning can be used for media and digital marketing operations, use Bayesian methods for A/B testing, develop an understanding of the difference between conventional and Bayesian A/B testing, use adaptive algorithms and optimize A/B testing results, distinguish between Bayesian and frequentist statistics.
The syllabus
Introduction and Outline
The High-Level Picture
- Real-World Examples of A/B Testing
- What is Bayesian Machine Learning?
Bayes Rule and Probability
- Review Section Introduction
- Probability and Bayes' Rule Review
- Calculating Probabilities - Practice
- The Gambler
- The Monty Hall Problem
- Maximum Likelihood Estimation - Bernoulli
- Click-Through Rates (CTR)
- Maximum Likelihood Estimation - Gaussian (pt 1)
- Maximum Likelihood Estimation - Gaussian (pt 2)
- CDFs and Percentiles
- Probability Review in Code
- Probability Review Section Summary
- Beginners: Fix Your Understanding of Statistics vs Machine Learning
- Suggestion Box
Traditional A/B Testing
- Confidence Intervals (pt 1) - Intuition
- Confidence Intervals (pt 2) - Beginner Level
- Confidence Intervals (pt 3) - Intermediate Level
- Confidence Intervals (pt 4) - Intermediate Level
- Confidence Intervals (pt 5) - Intermediate Level
- Confidence Intervals Code
- Hypothesis Testing - Examples
- Statistical Significance
- Hypothesis Testing - The API Approach
- Hypothesis Testing - Accept Or Reject?
- Hypothesis Testing - Further Examples
- Z-Test Theory (pt 1)
- Z-Test Theory (pt 2)
- Z-Test Code (pt 1)
- Z-Test Code (pt 2)
- A/B Test Exercise
- Classical A/B Testing Section Summary
Bayesian A/B Testing
- Section Introduction: The Explore-Exploit Dilemma
- Applications of the explore- exploit dilemma
- Epsilon Greedy Theory
- Calculating a Sample Mean (pt 1)
- Epsilon-Greedy Beginner's Exercise Prompt
- Designing Your Bandit Program
- Epsilon-Greedy in Code
- Comparing Different Epsilons
- Optimistic Initial Values Theory
- Optimistic Initial Values Beginner's Exercise Prompt
- Optimistic Initial Values Code
- UCB1 Theory
- UCB1 Beginner's Exercise Prompt
- UCB1 Code
- Bayesian Bandits / Thompson Sampling Theory (pt 1)
- Bayesian Bandits / Thompson Sampling Theory (pt 2)
- Thompson Sampling Beginner's Exercise Prompt
- Thompson Sampling Code
- Thompson Sampling With Gaussian Reward Theory
- Thompson Sampling With Gaussian Reward Code
- Why don't we just use a library?
- Nonstationary Bandits
- Bandit Summary, Real Data, and Online Learning
- (Optional) Alternative Bandit Designs
Bayesian A/B Testing Extension
- More about the Explore-Exploit Dilemma
- Confidence Interval Approximation vs. Beta Posterior
- Adaptive Ad Server Exercise
Practice Makes Perfect
- Intro to Exercises on Conjugate Priors
- Exercise: Die Roll
- The most important quiz of all - Obtaining an infinite amount of practice
Setting Up Your Environment (FAQ by Student Request)
- Anaconda Environment Setup
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Extra Help With Python Coding for Beginners (FAQ by Student Request)
- How to Code by Yourself (part 1)
- How to Code by Yourself (part 2)
- Proof that using Jupyter Notebook is the same as not using it
- Python 2 vs Python 3
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
- How to Succeed in this Course (Long Version)
- Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
- Machine Learning and AI Prerequisite Roadmap (pt 1)
- Machine Learning and AI Prerequisite Roadmap (pt 2)
Appendix / FAQ Finale
- What is the Appendix?
- BONUS
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