Machine learning is all around us. From antilock braking systems to autopilot systems in airplanes and cars, smart speakers, serve as personal digital assistants, to systems that learn our movie preferences and recommend what to watch next. Machine learning has become ubiquitous in our lives

Machine learning is a data analysis method that automates analytic model building. It is a branch of artificial intelligence based on the idea that systems can learn from historical data they can identify some patterns in the data and make business decisions with minimal human intervention.

Machine learning is an application of artificial intelligence that provides systems the ability to learn and improve from experience without being programmed. It focuses on the development of computer programs that can access data and use it to learn for themselves and then predict

It was born from the pattern recognition and theory that a computer cannot without being programmed to perform specific tasks and is based on an algorithm that can learn from data without relying on rule-based programming machine learning.

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There is input then there are some instructions that work on that Input and then we have an output and that instructions can be called a model and what really happened that we learn it from the historical data and build a model and then any new data that comes in that model we will apply that model (also called Scoring) and then given output

There are many Three Types of machine learning supervised, reinforcement, and unsupervised

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In supervised learning, we have independent variables which are a set of features that talk about a particular business case. For example, You are an analyst of a Bank and doing a study on loan default. We have a dependent variable that tells whether a customer will do a loan default.

So both dependent and independent variables are inputs to the machine and the machine learns from this particular data and then it builds a model. So supervised learning is just like that there is supervision, there are already a set of labels. The model is run on the train data and then run on test data and then we see what is the accuracy that is correct prediction by the number of the test instances

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The other type of learning is unsupervised learning wherein there are no labels we don’t know is it a yes or no.

For example, In customer segmentation, we are segmenting customers for doing marketing campaigns. We study features like spending, income, and age. We can segment the customer and we don’t have any answers or labels. We just received the unknown data and we are trying to learn from it. So this is the process of unsupervised learning that there is no label.

Reinforcement learning is a type of learning in which you learn and then for any corrective action you get a positive point and for anything wrong action you get the negative point. Examples of reinforcement learning can be something like an automated driving

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There are two primary entities, the agent and the environment. The agent figures out the best way to accomplish a task through a series of cycles in which the agent takes an action and receives immediate positive or negative feedback on the action from the environment.

Agree? Kindly comment on your experience with machine learning and its different types.

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