Generative models are a class of machine learning models that are designed to generate new data that is similar to the training data. They can be used for a variety of tasks, such as image synthesis, text generation, and anomaly detection. Generative Adversarial Networks (GANs): GANs are a type of generative model that consist of two parts: a generator network and a discriminator network. The generator network generates new data, and the discriminator network tries to distinguish the generated data from the real data. The two networks are trained together in an adversarial manner, with the generator trying to generate data that can fool the discriminator and the discriminator trying to correctly identify the generated data.

With Machine learning all over the place, it is becoming increasingly important to capture best practices and solutions to tackle common ML problems. Design patterns are a way of capturing these problems and providing reusable answers using generic and well-proven ML designs. They are ways of thinking when designing solutions or building ML systems. Now the question is, Can we abstract best machine learning practices into design patterns? As per Wikipedia, "Design patterns are formalized best practices that the programmer can use to solve common problems when designing an application or system".

"Critical thinking is an active and ongoing process. It requires that we all think like Bayesians, updating our knowledge as new information comes in." ― Daniel J. Levitin, A Field Guide to Lies: Critical Thinking in the Information Age Before we delve into the intuition behind using the Bayesian approach of estimation, we need to understand a few concepts. Inferential statistics is when you infer something about a whole population based on a sample of that population, as opposed to descriptive statistics which describes something about the whole population. When it comes to inferential statistics, there are two main philosophies: frequentist inference and Bayesian inference. The frequentist approach is known to be the more traditional approach to statistical inference, and thus studied more in most statistics courses (especially introductory courses). However, many would argue that the Bayesian approach is much closer to the way humans naturally perceive probability.

We all love sport and in our life one time we all wish to get the professional training from the coaches. For everyone it is not possible to afford such fees of the coaches. So we have come with the idea as follows. In every sport the main focus is on angles of the athlete what should be the angle between the hands and legs while doing particular activity. So how it will be if we can detect the angles from the video or photo while doing the particular activity and that angles we can send to the coaches and they can guide the athlete about their mistake and it will help them to improve.

Running high-volume manufacturing smoothly will involve the perfect functioning of all the machines, resulting in efficient production. The objective of any manufacturing unit is to keep operations at optimum speed with very less downtime. However, every equipment goes through wear and tear and needs servicing and maintenance periodically. The critical question here is, when is the best time to conduct equipment maintenance. Scheduled maintenance is effective or damage control in case of machine failure?

In this approach, technology is used to perform a job that used to be the primary activity of a person. Examples: replacing bank tellers with automated teller machines or call center workers with interactive voice response systems. This approach involves atomizing, or breaking up, a job into pieces, and automating as much as possible, leaving humans to do the non-automatable pieces and possibly supervise the automation. The transition of artisans to assembly line workers is an example of this. Relying on machine language translation and leaving professionals to "clean up" the results is another.