Hands-On Bayesian Methods with Python

This is the code repository for [Hands-On Bayesian Methods with Python Video, published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.

About the Video Course

Bayesian methods have grown recently because of their success in solving hard data analytics problems. They are rapidly becoming a must-have in every data scientists toolkit. The course uses a hands-on method to teach you how to use Bayesian methods to solve data analytics problems in the real world. You will understand the principles of estimation, inference, and hypothesis testing using the Bayesian framework. You will also learn to use them to solve problems such as A/B testing, understanding consumer habits, risk evaluation, adjusting machine learning predictions, reliability analysis, detecting the influence of one variable over an outcome, and many others. By taking this course, you will be able to apply and use Bayesian methods as part of your data analytics toolbox, thus helping you use Python to solve a majority of common statistical problems in data science.

What You Will Learn

Solve interesting statistical and data analytics problems using Python and the Bayesian approach.
Use the PyMC3 library for data analysis and modeling.
Core concepts and approaches to using Bayesian Statistics.
Utilize the Bayesian Theorem to use evidence to update your beliefs about uncertain events.
Solve problems arising in many quantitative fields using Bayesian inference and hypothesis testing.
Improve the performance and interpretation of the results of predictive models by using Bayesian methods.

Instructions and Navigation

Assumed Knowledge

To fully benefit from the coverage included in this course, you will need:
Some knowledge of statistics and Python programming skills is mandatory.

Technical Requirements

This course has the following software requirements:
For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:

OS: Windows 10, Mac OS X 10.7 Lion. Ubuntu 16 Processor: Intel core i5 Memory: 4GB Storage: 6GB

Software Requirements

Operating system: Windows 10, Mac OS X 10.7 Lion. Ubuntu 16 Browser: latest Chrome, Firefox Python 3.6 Anaconda distribution (recommended) Up-to-date (early 2018) versions of NumPy, Scipy, Pandas, Matplotlib, Seaborn and PyMC3.

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