Machine learning is a “Field of study that gives computers the ability to learn without being explicitly programmed.” It identifies patterns from data and helps to make a decision with minimal human intervention.

Two major areas in machine learning are –

• • • Supervised Learning – Here, it is mainly prediction based on user input. Data are placed on the correct label. We know the sample data relationships and based on it, we predict the outcomes. It is usually done in classification (to label the data) and regression (to get the correct outcome from labelled data). Classification helps to classify data in different classes. It is discrete and doesn’t imply any form of order. Regression helps to predict the value of future outcomes by using past data. This method helps in forecasting the data and Time-series Analysis.
    • Unsupervised Learning – Here, the data is neither labelled nor sorted. It mainly focuses on discovering the patterns of data. The output is dependent on coded algorithms. Supervised learning is more applicable in real life problems as unsupervised learning gives poor and inaccurate outcomes. It is further classified into clustering and association. Clustering is a technique to cluster or subgroup the data into similar traits. To find the relationship between two or more data variables we use measures of association.