Introduction to Machine Learning for beginners
Machine Learning for beginners

Introduction to Machine Learning for beginners

Learning may be defined as the process of improving one’s ability to perform a task efficiently. Machine learning is another sub field of computer science and data mining which enables modern computers to learn without being explicitly programmed.

Machine learning is a subset where a large computer of machines is used to analyse and extract knowledge from a large dataset.

Machine learning is closely related to computational statistics and it has strong ties to mathematical optimization, Machine learning explores the area of algorithms, which can make high-end predictions on data. Machine learning is employed in various types of computing task where designing efficient algorithms and program become rather difficult such as Email spam Filtering, Optical character recognition ( OCR), Search Engine Improvement, Computer Vision, Data Mining etc.

Machine learning system can be classified into three categories, depending on the nature of the learning signal available to a learning system

Unsupervised learning: No labels of outputs are given to the learning system. It finds structure on its own from the inputs given to, each example is a pair consisting of an input object vector and the desired output value supervisory signal.

Supervised learning: The system is presented with inputs and desired outputs by a human and the goal is to learn a model to map inputs to outputs, It is closely related to the problem of density estimation in statistics

Reinforcement learning: The system interacts with an environment in which it performs a stated goal without a human explicitly telling it whether it has come close to its goal. It is applied to diverse areas like game theory, information theory, swarm intelligence, statistics and genetic algorithms.

Types of machine learning models

We can broadly divide the preceding use cases and methods into two categories of machine learning:

Supervised learning: These types of models use labeled data to learn. Recommendation engines, regression, and classification are examples of supervised learning methods. The labels in these models can be user–movie ratings for the recommendation, movie tags in the case of the preceding classification example, or revenue figures for regression.

Unsupervised learning: When a model does not require labeled data, we refer to unsupervised learning. These types of models try to learn or extract some underlying structure in the data or reduce the data down to its most important features. Clustering, dimensionality reduction, and some forms of feature extraction, such as text processing.

The application of machine learning to diverse areas of computing is getting popularity rapidly, not only because of cheap and powerful hardware, but also because of the increasing availability of free and open source software, which enable machine learning to be implemented easily. Machine learning practitioners and researchers, being a part of the software engineering team, continuously build sophisticated products, integrating intelligent algorithms with the final product to make software work more reliably, quickly and without hassles.