Learn the concepts and practical side of using the Bayesian approach to estimate likely event outcomes. 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.