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Bayesian Data Analysis in Python

Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!

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4 Horas14 Videos49 Exercicios
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Descrição do Curso

Bayesian data analysis is an increasingly popular method of statistical inference, used to determine conditional probability without having to rely on fixed constants such as confidence levels or p-values. In this course, you’ll learn how Bayesian data analysis works, how it differs from the classical approach, and why it’s an indispensable part of your data science toolbox. You’ll get to grips with A/B testing, decision analysis, and linear regression modeling using a Bayesian approach as you analyze real-world advertising, sales, and bike rental data. Finally, you’ll get hands-on with the PyMC3 library, which will make it easier for you to design, fit, and interpret Bayesian models.
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Nas seguintes faixas

Applied Statistics in Python

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  1. 1

    The Bayesian way

    Grátis

    Take your first steps in the Bayesian world. In this chapter, you’ll be introduced to the basic concepts of probability and statistical distributions, as well as to the famous Bayes' Theorem, the cornerstone of Bayesian methods. Finally, you’ll build your first Bayesian model to draw conclusions from randomized coin tosses.

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    Who is Bayes? What is Bayes?
    50 xp
    Bayesians vs. Frequentists
    100 xp
    Probability distributions
    100 xp
    Probability and Bayes' Theorem
    50 xp
    Let's play cards
    100 xp
    Bayesian spam filter
    100 xp
    What does the test say?
    50 xp
    Tasting the Bayes
    50 xp
    Tossing a coin
    100 xp
    The more you toss, the more you learn
    100 xp
    Hey, is this coin fair?
    100 xp
  2. 2

    Bayesian estimation

    It’s time to look under the Bayesian hood. You’ll learn how to apply Bayes' Theorem to drug-effectiveness data to estimate the parameters of probability distributions using the grid approximation technique, and update these estimates as new data become available. Next, you’ll learn how to incorporate prior knowledge into the model before finally practicing the important skill of reporting results to a non-technical audience.

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  3. 3

    Bayesian inference

    Apply your newly acquired Bayesian data analysis skills to solve real-world business challenges. You’ll work with online sales marketing data to conduct A/B tests, decision analysis, and forecasting with linear regression models.

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  4. 4

    Bayesian linear regression with pyMC3

    In this final chapter, you’ll take advantage of the powerful PyMC3 package to easily fit Bayesian regression models, conduct sanity checks on a model's convergence, select between competing models, and generate predictions for new data. To wrap up, you’ll apply what you’ve learned to find the optimal price for avocados in a Bayesian data analysis case study. Good luck!

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Applied Statistics in Python

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Conjuntos De Dados

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Colaboradores

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Amy Peterson
Collaborator's avatar
Justin Saddlemyer
Michał Oleszak HeadshotMichał Oleszak

Machine Learning Engineer

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