INTRODUCTION TO MACHINE LEARNING

Machine Learning was first defined by Arthur Samuel in early 90's describing it as,” A field of study that gives the ability to the computer for self-learn without being explicitly programmed”, that means giving machines information without hard-coding it.

"A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." - Tom Mitchell

Machine learning focuses on data-driven learning based on actual interactions and is the process of teaching computers and digital devices to learn and carry out tasks the same way humans do.

Machine learning (ML), a subset of the widely adopted idea of Artificial Intelligence (AI), transforms data into knowledge for programs and applications that provide computers the ability to do human-like tasks. This data helps machines function better over time and increases their accuracy all the while.

Types of Machine Learning:

Machine Learning can be broadly classified as:

• Supervised Machine Learning: Supervised learning is most often employed category of machine learning. In this learning, labeled data is used to train the machine learning algorithm. Such algorithm uses labeled samples to predict future events by applying knowledge from the past to fresh data. The weights are adjusted until the model is well fitted when input data is inputted into it. Regression and classification algorithms are used in supervised learning to make predictions or divide data into distinct classes.
• Unsupervised Machine Learning: Unsupervised machine learning includes building models using data without labels or clearly stated outcomes. These algorithms look for concealed patterns or data clusters without human interaction. As this method may identify same and different patterns in data, it is useful for exploratory data analysis, consumer segmentation, cross-selling strategies, and the finding of images and patterns. Clustering and association techniques are utilized to implement the models in unsupervised learning.
• Semi-Supervised Machine Learning: This learning falls in between supervised and unsupervised learning. It uses a small amount of labeled dataset during training in order to manage feature selection or extraction and classification from a larger set of unlabeled data. Semi-supervised learning can be used to provide a best solution to the problem of not having enough labeled data. It also helps in case of labeling more data which would be beneficial but too expensive.