INTRODUCTION

• WHAT IS MACHINE LEARNING ?
  • Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data.
    
    
    
• WHAT IS THE USE OF MACHINE LEARNING ?
  • Classification : classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.
    Ex : Classification using neural network.
  • Pattern recognition : Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition systems are in many cases trained from labeled "training" data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning).
    Ex : Pattern recognition to predict stock price
  • Prediction : Machine learning is used as a prediction tool in various fields.
    Ex : Weather predictions
  • Optimization : Usually the goal of classification is to minimize the test error.
    Ex : Convex optimization
  • Sentimental Analysis : Sentimental Analysis is nothing but differentiation between good or bad or sentiment. It is possible to find whether a sentence is positive sentence or negative sentence. It is also possible to find various emotions of humans using sentimental analysis
    Ex : Naive Bayes algorithm for sentimental analysis.

• HOW IT CAN BE USED ?
  • Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning "signal" or "feedback" available to a learning system. These are

• Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
• Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
• Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). The program is provided feedback in terms of rewards and punishments as it navigates its problem space.
  

• Between supervised and unsupervised learning is semi-supervised learning, where the teacher gives an incomplete training signal: a training set with some (often many) of the target outputs missing. Transduction is a special case of this principle where the entire set of problem instances is known at learning time, except that part of the targets are missing.