Bayesian Statistics & Supervised Machine Learning: A/B Testing

Through this training we are going to apply Bayesian methods to A/B testing and also use adaptive algorithms to improve A/B testing performance.

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 57m | 8 Videos | 67933 Views |   Intermediate| English[Auto-generated]

Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.

Through this training we are going to apply Bayesian methods to A/B testing and also use adaptive algorithms to improve A/B testing performance.

The training will include the following;

– Naive Bayes Classifier introduction
– Use of naive bayes in Machine Learning
– Understanding A/B testing
– Split tests
– Power of A/B and testing
– Example solving in Python using dummy data

Target Customers:

• Anyone who wants to learn about data and analytics
• Data Engineers
• Analysts
• Architects
• Software Engineers
• IT operations
• Technical managers

Pre-Requisites:

• Prior knowledge of machine learning required
• Basic knowledge of Python programming and statistics

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