What you'll learn:
PyMC3.
posterior
ROPE
Loss functions
Gaussian
Gaussian inferences
Student's t-distribution
Groups comparison
Hierarchical models
Shrinkage
Linear models and high autocorrelation
Pearson correlation coefficient
Pearson coefficient from a multivariate Gaussian
Robust linear regression
Hierarchical linear regression
Correlation, causation, and the messiness of life
Polynomial regression
Confounding variables and redundant variables
Masking effect variables
Variable variance
Adding interactions
Logistic regression
Multiple logistic regression
Dealing with correlated variables
Dealing with unbalanced classes
Softmax regression
Discriminative and generative models
the zero-inflated Poisson model
Posterior predictive checks
Occam's razor - simplicity and accuracy
Model averaging
Bayes factors
Non-identifiability of mixture models
How to choose K values

Requirements
Python knowledge is required

Description
This course is a comprehensive guide to Bayesian Statistics. It includes video explanations along with real life illustrations, examples, numerical problems, and take away notes. The course covers the basic theory behind probabilistic and Bayesian modelling, and their applications to common problems in data science, business, and applied sciences.

The course is divided into the following sections:

Section 2 and 3: These two sections cover the concepts that are crucial to understand the basics of Bayesian Statistics-

Introduction to Bayesian Probability

Introduction to PyMC3 primer

Summarizing the posterior.

Introduction to ROPE.

introduction to Gaussian.

Student's t-distribution.

Hierarchical models Introduction.

Linear models and high autocorrelation.

Introduction to Pearson coefficient from a multivariate Gaussian.

Robust linear regression.

Hierarchical linear regression.

Correlation, causation, and the messiness of life.

Polynomial regression.

Introduction to Confounding variables and redundant variables.

Masking effect variables.

Adding interactions.

Variable variance.

Section 4: This section covers Linear model generalization:

Introduction to Generalizing linear models.

Introduction to Logistic regression.

Applying the logistic regression to The Iris dataset.

Multiple logistic regression.

Interpreting the coefficients of a logistic regression.

Dealing with correlated variables.

Dealing with unbalanced classes.

Introduction to Softmax regression.

Introduction to Discriminative and generative models.

Introduction to Poisson regression.

Introduction to The zero-inflated Poisson model.

Section 5: This section covers Model Comparison:

Posterior predictive checks Implementation.

Occam's razor - simplicity and accuracy.

Model comparison with PyMC3.

Introduction to Bayes factors.

Bayes factors Implementation.

Common problems when computing Bayes factors and solutions.

Regularizing priors.

Section 6: This section covers Mixture Models

Introduction to Finite mixture models and its implementation.

How to choose K values.

Comparing models.

Mixture models and clustering.

Introduction to Continuous mixtures

At the end of the course, you will have a complete understanding of Bayesian concepts from scratch. You will know how to effectively use Bayesian approach and think probabilistically. Enrolling in this course will make it easier for you to score well in your exams or apply Bayesian approach elsewhere.

Complete this course, master the principles, and join the queue of top Statistics students all around the world.

Who this course is for
The course is ideal for anyone interested in learning both the conceptual and practical side of using Bayes' Rule to model likely event outcomes.
The course is best suited for both students and professionals who currently make use of quantitative or probabilistic modelling.
Students currently pursuing Statistics and Probability.
Anyone who wants to build a strong fundamental of Bayesian Statistics.
Anyone who wants to apply Bayesian Statistics to other fields like ML, Artificial Intelligence, Business, Applied Sciences, Psychology. etc.
Students of Machine Learning and Data Science.
Data Scientists curious about Bayesian Statistics.

Citación :

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