Machine learning course in Hyderabad

BASICS OF MACHINE LEARNING

Machine learning is a technique/study of algorithms that makes systems capable to learn from their past data and improve its performance more than before without any explicit programming. It is a subset of Artificial Intelligence (AI). It was developed to provide machines their own ability to learn new things as humans could and perform a specific task with perfection for a longer period of time as compared to humans. 

Machine Learning (ML) is playing a very crucial role in our day to day digital life. To understand it clearly, let’s see a few common examples. With Amazon, while we can search mostly every product we need, suppose we are searching for clothes. Then after a few moments, we could see the pop-ups showing the clothes recommended for you. It is just because of machine learning. By using many mathematical algorithms and statistics, the software could analyze the searching patterns of the user and on the basis of this, it shows the pop-up for the recommendation. 

Google Maps also uses machine learning to predict the traffic on the routes and at what time you would reach a destination according to the speed you are moving. There are many more features on Google maps that are being used for prediction.

Categories of Machine Learning

Below are a few major categories of machine learning algorithms:

Supervised Learning

The term ‘supervised’ means that the dataset which is used to teach machines are in a proper format. The format could be any form required. Supervised learning makes it easier for a machine to learn and get trained. Here learning means that the machine itself will build some logic. For supervised learning, we need data scientists as well as data analysts to get the required output or the given input with some skills of machine learning too. “Supervised Learning is a classified dataset which is used by data scientists and data analysts to train the machine to get the required output as per the given input”.

Supervised learning can be classified mainly in two categories:

• Classification
• Regression

A few types of supervised algorithms used are listed below:

• Linear Regression
• Neural Network
• Decision tree
• Naïve Bayes
• Nearest Neighbor

Unsupervised Learning

In unsupervised learning, there is no classified dataset which means that the dataset is not in a proper format (neither it is labeled, nor it is classified). The machine itself has to figure out how to learn. It is just opposite to supervised learning, but uses powerful algorithms to get the desired output as almost all data is unlabeled or unclassified so unsupervised learning can be an interesting area to provide training to machines. This type of learning needs lots and lots of data and when the machine gets trained, then it could group/organize the similar data in one unit in a human’s understandable format. 

Unsupervised learning algorithm techniques:

• Clustering
• Association

 A few types of unsupervised learning algorithms used are listed below:

• K-means clustering
• Association rule 
• Principle Component Analysis

Semi-Supervised Learning

The working of semi-supervised learning lies between supervised and unsupervised learning. In this type of learning, the major portion of the data is unclassified, and the rest of the data is in a classified format. So, first we use the unsupervised learning technique to get the data in a labeled form and after that, we use supervised learning techniques on the labeled data to get the desired output.

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