Common questions about Machine Learning

What is machine learning?

Machine learning is a type of, ai aka artificial intelligence that empowers a system to learn and make decisions itself without being programmed. These algorithms make the computer smart enough so that it can make choices on the basis of the data it has without any human intervention. The primary aim is to make algorithms that allow a system to learn and make their own decisions in future, based on the past data two why do we need machine learning.

Prediction while traveling

we all have been using gps system while traveling in our lives. whenever you book a cab it tells you the approximated fare and time required to reach your destination.

Search Engine Optimization

web search engines automatically show you the accurate results based upon your location in past searches. Programmers don’t program it to show you those results, but it gives accurate results within seconds according to your interests in recent searches

Span Mail Classification

in our email boxes, the system automatically classifies some emails as spam or junk mails and some mails as primary mails that could be very important for us. The system is never wrong and it is all possible with the help of these learning.

Supervised Machine Learning

learning is one of the most popular types of machine learning and it is easy to understand and implement. In this type, the algorithm is trained on given data but in the data needs to be labeled. You allow the system to predict the data and you make corrections if the predictions it makes are not accurate enough.

Unsupervised Machine Learning

unsupervised machine learning works without any labeled data but you have to provide a lot of data so that the system understands the properties that provide a base for the decision it has to make. This can improve the productivity in a lot of fields.

Reinforcement Learning

it is based upon trial and error methods. The system makes mistakes and learns from them in order to avoid these mistakes again. For example, in a maze, when the system fails to find a path it won’t go on the same path again because it knows that the path doesn’t work. It labels positive outcomes and negative outcomes and runs on the basis of these outcomes.

Machine Learning | Deep Learning | Kaggle Expert | Computer Engineering Student

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