Machine learning is a way to program a machine such that it can perform operation automatically through previous experience or past result.
In other words, we simply train an algorithm with a large set of data such that various parameters are being organized according to the input values and outputs are mapped accordingly for it. When we feed with real data, then from the previous optimization and history, it predicts the present values. And the result can be improved by tuning the parameters on the dataset. We feed a large set of data, these are divided into train data and test data. Our algorithm learns from train data and we predict the accuracy and precision of our algorithm on Test Data.
Machine learning is closely related to computational statistics, which focuses on making predictions using computers. It also focuses on mathematical optimization of methods, theory and application domains to the field of machine learning.
Types of Machine Learning
Machine Learning can be broadly classified into 3 types:
In this, a predefined output is given to the algorithm, such that it distinguishes or map the values easily. In simple word, outputs are known in the dataset. They too are of two types:
- Linear Regression: Here values that are being mapped are in a definite range.
- Logistic Regression: Here the values are either 0 or 1 or it can be True or False only.
In this type of learning, outputs are not known and data is provided to algorithm. It creates cluster within dataset and identifies them separately such that each cluster have its own unique characteristics.
This type of learning is a combination of both supervised and unsupervised learning and also they are based on win or loss system, the machine is simply run on values such that if the specific objective is achieved then a reward is given and it learns that otherwise, it try another approach.
For example, a robot is being placed on a table, on one side of it there is water and on the other side, there is fire. When Robot moves towards the fire, no award is given or loss is found and hence it learns to avoid it in future and when it moves toward the water then it gets awarded.
Machine Learning has a great scope in Future. In real life example, you can see in Gmail (Google App), it automatically marks spam emails and moves them towards trash.