Supervised Learning vs Unsupervised Learning

what is supervised learning

Supervised learning, you train the machine using data which is well “labeled.” It means some data is already tagged with the correct answer. like the data set has the inputs and also the relevant outputs. It can be compared to learning which takes place in the presence of a programmer or data scientist. if we see it real example ,which is similar to as a student learns things in the presence of a teacher.

Supervised learning can be used for two types of problems: Classification and Regression.

Classification problems ask the algorithm to predict a discrete value, identifying the input data as the member of a particular class like predicting dog images.

Regression problems look at continuous data . for example predicting the house prices.

These includes various algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, Multi-class Classification, Decision tree, Bayesian Logic,

Advantages of Supervised Learning?

• Supervised learning allows you to collect data or produce a data output from the previous experience.
• Helps you to optimize performance criteria using experience
• Supervised machine learning helps you to solve various types of real-world computation problem
• Supervised learning model produces an accurate result.

What is Unsupervised Learning

Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Instead, we need to allow the model to work on its own to discover information and patterns . It mainly works with the unlabelled data. Unsupervised learning algorithms allow you to perform more complex processing tasks compared to supervised learning. in that training dataset is a collection of examples without a specific desired outcome

The unsupervised learning model can organize the data in different ways

• Clustering:
  Without being an expert Microbiologist, it’s possible to look at a collection of Fish photos and separate them roughly by species, relying on cues like color, size or shape. That’s how the most common application clustering, works: the deep learning model looks for training data that are similar to each other and groups them together
• Anomaly detection: Banks detect fraudulent transactions by looking for unusual patterns in customer’s purchasing behavior.unsupervised learning can be used to flag outliers in a dataset. when purchase patters of Credit card is suspicious
• Association: Search in a shopping site about gaming product and you will getting popups on gaming items its by looking at a couple key attributes of a data point, an unsupervised learning model can predict the other attributes with which they’re commonly associated.
• Autoencoders: Autoencoders take input data, compress it into a code, then try to recreate the input data from that summarized code. autoencoders can remove noise from visual data like images, video or medical scans to improve picture quality.

Advantages of Unsupervised Learning?

• Unsupervised machine learning finds all kind of unknown patterns in data.
• Unsupervised methods help you to find features which can be useful for categorization.
• It is taken place in real time, so all the input data to be analyzed and labeled in the presence of learners.
• It is easier to get unlabeled data from a computer than labeled data, which need help of humans.
• It includes various algorithms such as Clustering, KNN, and Apriori algorithm

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