What is Bayesian?

Bayesian analysis is a statistical paradigm that uses probability assertions to answer research queries regarding unknown parameters.

Because the basic premise is that all parameters are random values, probabilistic assertions are natural in Bayesian analysis. A parameter in Bayesian analysis is summarised by an entire distribution of values rather than a single fixed amount as in classical frequency analysis. Finding this distribution, a posterior distribution of an interesting parameter is fundamental to Bayesian analysis.

A posterior distribution is made up of a previous distribution and a probability model that provides information about the parameter based on observed data. The posterior distribution is either accessible analytically or estimated by one of the Markov chain Monte Carlo (MCMC) techniques, depending on the prior distribution and likelihood model used.

The posterior distribution is used in Bayesian inference to generate various summaries for model parameters, such as point estimates such as posterior means, medians, percentiles, and interval estimates known as credible intervals. Furthermore, all statistical tests on model parameters may be written as probability assertions using the determined posterior distribution.

The ability to incorporate prior information into the analysis, an intuitive interpretation of credible intervals as set ranges to which a criterion is known to belong with a prespecified probability, and the ability to assign an actual probability to any hypothesis of interest are all distinguishing features of Bayesian analysis.

What is machine learning?

Machine learning (ML) is the study of computer algorithms that can improve themselves automatically based on experience and data. It is regarded as a component of artificial intelligence. Machine learning algorithms construct a model using sample data, referred to as training data, in order to make predictions or judgments without being explicitly programmed to do so. Machine learning algorithms are utilized in a broad range of applications, including medicine, email filtering, speech recognition, and computer vision, when developing traditional algorithms to execute the required tasks would be difficult or impossible.

Machine learning is closely connected to computer science, which focuses on generating predictions with computers; however, not all machine learning is statistical learning. Mathematical optimization research provides tools, theory, and application fields to the subject of machine learning. A similar topic of research is data mining, which focuses on exploratory data analysis via unsupervised learning. Some machine learning implementations employ data and neural networks to replicate the operation of a biological brain. Machine learning is sometimes known as predictive analytics when applied to commercial concerns.

What is Bayesian Machine Learning?

According to the Bayesian paradigm for machine learning, you begin by listing all plausible models of the data and assigning your prior belief P(M) to each of these models. Then, after viewing the data D, you construct P(D|M) by evaluating how likely the data was under each of these models.

What is Python?

Python is a general-purpose, high-level, interpreted programming language. Its design philosophy prioritizes code readability through the use of substantial indentation.

Python is garbage-collected and dynamically typed. It supports a wide range of programming paradigms, including structured (especially procedural), object-oriented, and functional programming. Because of its extensive standard library, it is frequently referred to as a "batteries included" language.

Python is a well-known general-purpose programming language that may be used for a wide range of tasks. It has high-level data structures, dynamic typing, dynamic binding, and many other capabilities that make it ideal for both complicated program development and scripting or "glue code" that links components. It may also be expanded to make system calls to practically any operating system and run C or C++ programs. Python is a global language used in a wide range of applications due to its ubiquity and ability to operate on practically every system architecture.

What is ab testing?

An A/B Test is a controlled experiment in which two groups, A and B, are given distinct experiences. The goal of an A/B Test is to understand and quantify the reaction of each group.

You may use the Two-Sample hypothesis test or the Independent Samples t-test to assess and interpret the outcomes of your A/B tests.

"The two-sample t-test (also known as the independent samples t-test) is a method for determining whether two groups' unknown population means are equal or not."

What is the example?

Some instances of A/B Testing in action

Strategies for online advertising

When contacting consumers, use email subject lines.

The effect of mailing a discount to consumers vs. a control group was investigated.

Netflix is experimenting with several visuals for the same film.

Amazon is experimenting with new website features in order to remain ahead of the competition.

What is this course about?

A/B testing is the focus of this course.

A/B testing is utilized all over the place. Marketing, retail, newsfeeds, internet advertising, and other services are available.

It is all about comparing things in A/B testing.

If you're a data scientist and want to convince the rest of the organization that "logo A is superior to logo B," you can't just say it without backing it up with stats and statistics.

Traditional A/B testing has been around for a long time and is riddled with approximations and ambiguous concepts.

While we will undertake traditional A/B testing in this course to comprehend its complexity, we will ultimately come to the Bayesian machine learning method of doing things.

First, we'll explore if adaptive approaches can improve standard A/B testing. All of these things assist you in resolving the explore-exploit conundrum.

You will be introduced to the epsilon-greedy algorithm, which you may have heard of in the context of reinforcement learning.

We'll improve on the epsilon-greedy method by employing a comparable approach known as UCB1.

Finally, we'll improve on both of those by employing a completely Bayesian strategy.

Why is the Bayesian method interesting to us in machine learning?

It's a whole different method of approaching probability.

It represents a paradigm change.

You'll most likely need to revisit this course numerous times before it really sinks in.

It's also quite powerful, and many machine learning professionals frequently remark that they "adhere to the Bayesian school of thought."

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