WHAT IS MACHINE LEARNING?

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a part and parcel of Artificial Intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers.

                      The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.

TYPES OF MACHINE LEARNING

Generally, the machine-learning process classifies into three categories – Supervised, Unsupervised and Reinforcement Learning.

1.      Supervised Machine Learning – This form of machine learning learns from labeled data and takes actions. For example, consider a dataset where different attributes of a set of flowers are collected. Using these attributes one can identify a category of a flower family

2.      Unsupervised Machine Learning – This form of machine learning learns from unlabelled data and takes actions. For example, consider a dataset containing attributes of all the houses in a given country or state or city. Using attributes like size in square feet, amenities, parks near-by, schools near-by, etc. it is not possible to predict a given house. In addition, even if it is, the intent of prediction is to predict the price of a given house based on attributes and not which house the attributes belong.

3.      Reinforcement Learning – This form of machine learning learns from rewards based system depending upon the actions performed by the model. This is the most advanced form of machine learning which applies to artificial intelligence based systems like neural network, robotics, and recommendation engines.

USES OF MACHINE LEARNING

There are endless applications of machine learning and limitless advantages for machine learning.

Image recognition: The image recognition is one of the most common uses of machine learning applications. It can also be referred to as a digital image and for these images; the measurement describes the output of every pixel in an image. The face recognition is also one of the great features that have been developed by machine learning only. It helps to recognize the face and send the notifications related to that to people.

Voice recognition: Machine learning (ML) also helps in developing the application for voice recognition. It also referred to as virtual personal assistants (VPA). It will help you to find the information when asked over the voice. After your question, that assistant will look out for the data or the information that has been asked by you and collect the required information to provide you with the best answer. There are many devices available in today’s world of Machine learning for voice recognition that is Amazon echo and Google home is the smart speakers. There is one mobile app called Google allo and smartphones are Samsung S8 and Bixby.

Predictions: Machine learning helps in building the applications that predict the price of cab or travel for particular duration and congestion of traffic where can be found. While booking the cab and the app estimates the approximate price of the trip that is done by the uses of machine learning only. When do we use GPS service to check the route from source to destination, the app will show us the various ways to go and check the traffic on that moment for the lesser number of vehicles and where the congestion of traffic is more that is done or retrieved by the uses of machine learning application.

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