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What is Machine Learning

In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions. But can a machine also learn from experiences or past data like a human does? So here comes the role of Machine Learning. Machine Learning (ML) in the field of study gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.

Uses of Machine Learning:

Now machine learning has got a great advancement in its research, and it is used everywhere around us, such as self-driving cars, Amazon Alexa, Catboats, recommender systems, and many more. It includes Supervised, unsupervised, and reinforcement learning with clustering, classification, decision tree, SVM algorithms, etc. Modern machine learning models can be used for making various predictions, including weather prediction, disease prediction, stock market analysis, etc.

How Machine Learning Works:

A Machine Learning system (1)learns from a dataset that has already some data and some outputs (say results) according to that data. (2) After that builds the prediction models with some machine learning algorithms where some codes act as spices. (3) Then whenever it receives new data, it predicts the output (say the result of that new data) for it. The accuracy of predicted output depends upon the amount of data, as the huge amount of data helps to build a better model which predicts the output more accurately.


So before diving deeper into this, you should have the fundamental knowledge of the following things, so that you can easily understand the entire topic and after getting quality knowledge you can imply machine learning anywhere you want.

· Fundamental knowledge of probability and linear algebra.

· The ability to code in any computer language, especially in Python language.

· Knowledge of Calculus, especially derivatives of a single variable and multivariate functions.

And last but not the least, the practice of keeping some patience.

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