Machine Learning

Machine Learning subset of artificial intelligence is the scientific study of algorithm and statistical mathematics that used by the machine to perform the desired task without explicitly design to perform that specific task or in other words it is a type of programming technique in which a machine doesn’t bound to perform only a certain or specific task. Machine Learning (ML) is just an algorithmic mathematical approach in which a machine is trained on a sample data also known as ‘Training Dataset’. To make predictions or decisions without being programmed explicitly to perform that specific task.

The name ‘Machine Learning’ was coined in 1959 by “Arthur Samuel” (An American pioneer in the field of computer gaming and AI). Machine Learning tasks are classified in several broad categories which are as follows:

• Supervised learning

  – It is a task-driven technique in which machine predict the next value.

When a machine is trained on a labelled dataset in which a function maps an input to output based on trained datasets based on training datasets examples input-output pairs.

(Labelled dataset is one which has input and output parameters).

Supervised Learning also sub-divided in furthermore two sections mainly:-

• Classification (Defined Labels)- It is a supervised learning technique in which output is already have defined labels.
  • Regression (No Labels Defined)- It is a supervised learning technique in which output always has continuous values.

Examples of supervised learning–

• Linear Regression
  • Nearest Neighbor
  • Gaussian Naive Bayes
  • Decision Trees
  • Support Vector Machine (SVM)
  • Random Forest

• Unsupervised Learning -It is a data-driven technique in which machine identify the clusters. Or in other words, it is an algorithmic approach in which no labelled data are given to the machine and leaving it on its own to learn from its experiences or to find the hidden pattern or structure to make a pattern or find a pattern or structure.

Examples of unsupervised learning–

• K-Means clustering problems
  • Apriori algorithm for association rule learning problems
• Semi-supervised Learning– It is a combination of supervised and unsupervised learning. In this technique, an algorithm learns from labelled data and unlabelled data (maximum dataset is unlabelled data and a small amount of labelled one) it falls in-between supervised and unsupervised learning algorithm.
  • Internet Content Classification
  • Speech Analysis
  • Protein Content Classification

Reinforcement Learning– As mentioned in the name itself, It is a technique in which machine reinforced or improved itself by previous experiences. A system interacts with a dynamic environment in which it must perform certain goals. In this algorithmic approach, feedback is given to the machine to perform the task. Here feedback can add or remove the data to increase the performance of the machine. The best-known example of machine learning is the navigation system of driverless cars.

Examples of reinforcement learning–

• Real-Time Decision
  • Robot/Automobile Navigation
  • Game AI
  • Learning Tasks
  • Skill Acquisition