Machine learning is a Subset of the largest field called as Artificial Intelligence. Basically Machine learning is that "focuses on teaching computers how to learn without need to be programmed for specific task."
The primary aim of machine learning is to allow the computers to learn automatically without human interaction and do actions accordingly. Actually, the key idea behind Machine learning is that it is possible to create algorithms and make predictions on data.
In order to enlighten the machine, you need the three main components as dataset, algorithm and features.
1. Dataset
In machine learning methods we typically need a few datasets for different purposes. Dataset is a collection of instances and instances is a single row of data. Data sets describe values for each variable for unknown quantities such as height, weight, temperature, volume and these values in the set is called as datum.
2. Feature
feature is an individual measurable characteristic of a phenomenon machine learning. Choosing informative, discriminating and independent features is a crucial step for effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition.
3. Algorithm
For achieving the better performance, algorithm plays a vital role.It is possible to solve the same task using different algorithms. Depending on the algorithm, the accuracy or speed of getting the results can be different. Any software that uses ML is more independent than manually encoded instructions for performing specific tasks. The system learns to recognize patterns and make valuable predictions. If the quality of the dataset was high, and the features were chosen right, an ML-powered system can become better at a given task than humans.
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