Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. The role of Machine learning in real world is increasing day by day as technology become advanced.
Characteristics of Machine Learning
- The ability to perform automated data visualization.
- The ability to take efficiency to the next level when merged with IoT.
- The ability to change the mortgage market.
- Accurate Data Analysis.
- Do Business Intelligent at its best.
ML Algorithms
- Supervised Learning Algorithm
- Semi-supervised Learning Algorithm
- Unsupervised Algorithm
- Reinforcement Learning Algorithm
1. Supervised Learning Algorithm
Supervised learning is the types of machine learning in which machines are trained using well “labelled” training data, and on basis of that data, machines predict the output. The labelled data means some input data is already tagged with the correct output.
Examples :
- Classification : Fraud Detection, Image Classification
- Regression : Risk Assessment, Score Prediction
2. Semi-supervised Learning Algorithm
Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. Semi-supervised learning falls between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data)
3. Unsupervised Algorithm
Unsupervised learning is a type of machine learning in which models are trained using unlabeled dataset and are allowed to act on that data without any supervision.
Examples :
- Dimensionality Reduction : Text Mining, Face Recognition etc
- Clustering : City Planning, Targetted Marketing
4. Reinforcement Learning Algorithm
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. . Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
e.g. Robot Navigation, Inventory Management, Gaming etc.
Summary
Machine learning is a subfield of computer science, but is often also referred to as predictive analytics, or predictive modeling. Its goal and usage is to build new and/or leverage existing algorithms to learn from data, in order to build generalizable models that give accurate predictions, or to find patterns, particularly with new and unseen similar data.
