About Machine Learning
Machines calculates and produce output results based on input data entered mostly through manual interventions. Researchers and scientists are working towards machine learning and developing the capability of artificial intelligence which includes decision making through prescriptive and predictive data analytics and also human like learning capability through vision, feel, bipedalism (i.e., ability to balance and walk on two legs just like humans), understand the surrounding environment and make decisions on their own. Machine learning is one of the applications of artificial intelligence which is aimed at giving the capability of think and learn to machines just like human beings.
Machines learning includes various types of computer programs and algorithms which help machines to understand the surrounding environment by capturing data through sensors. Computer algorithms decipher the data captured by sensors and convert them into relevant information. Machine learning is aimed at minimizing human intervention. It is also focused on making the computer programs to understand the data and use the information to learn and improvise on their own to take decisions.
Types of machine learning
1) Supervised learning
It is done under guidance of some mentor who knows each and every aspect of the particular topic. In case of machines, the computer programs or algorithms that run a machine, are fed with well-defined structured or labelled set of data through which a machine is able to understand and learn. When large amount of structured or labelled data is given to a machine again and again, then the machine learns to identify the fields of that data. This is known as supervised learning for machines.
2) Unsupervised learning
When learning does not require supervision or guidance then it is called unsupervised learning, i.e., learning without the help of a mentor. For unsupervised learning of machines, the computer program is given an input of unstructured or unlabelled data. In this case computer program may not know the exact and correct details of that data as no labeling of data is there, however, the computer program can learn on its own to identify similar patterns in the data and it can form clusters of similar data sets.
3) Semi – supervised learning:
It may have both types of data sets, i.e, labelled as well as unlabelled. Computer program can learn from labelled data sets and can use the knowledge to take out similar patterns from the unlabelled data sets with meaningful output.
4) Reinforcement learning
It is a constant feedback based learning. The computer program learns from the labelled data and gives an output. Then this output is taken as input for further improvement of results.
Most popular machine learning algorithms:
- Linear Regression
- Logistic Regression
- KNN Classification
- Support Vector Machine (SVM)
- Decision Tree
- Random Forest
- Artificial Neural Network (ANN)
- K – Means Clustering
- Naive Byes Theorem
- Recurrent Neural Network (RNN)