Machine Learning

Machine Learning: A New Paradigm in Data Analytics

As John Naisbitt said, “We are drowning in information and starving for knowledge”. Small or big many enterprises, handle deluge of data. It is hard to manually segregate and analyze this data. Thus, automated, but smart data analysis has become imperative in the regime of Big Data. Call it machine Intelligence, Artificial Intelligence or Cognitive computing, now it has entered the mainstream consciousness. As Machine Learning enters our workplaces, living spaces and lives, people are expecting IT to play an important role.

What is Machine Learning?

“Machine Learning is a set of methods designed elegantly for automatically detecting patterns in data. These uncovered patterns can be utilized for predicting future data, or to carry out other kinds of decision making under uncertainty like planning on different ways to collect increasing data”. Machine learning is a proven method of analyzing data, which is carried out by automating analytic model building. Machine Learning uses specifically designed algorithms that iteratively learn from data, thereby allowing computers to find hidden insights without the need of explicit programs.

Types of Machine Learning

Machine learning is divided into three major approaches ‘Predictive or Supervised Learning’, ‘Descriptive or Unsupervised Learning’ and ‘Reinforcement Learning’.

Supervised learning has a motive to learn the mapping from given inputs. Unsupervised learning is helpful in finding interesting patterns in the data. Whereas, Reinforcement learning is useful to understand how to act or behave upon receiving an occasional signal or punishments.

supervised machine learningSupervised Learning Approach: The goal of predictive or supervised learning is to let the computer learn the classification system we created. Moreover, it seems that classification learning is more appropriate where deducing a classification is useful and at the same time, classification is to determine. Remember, the probability of inputs is often left undefined by the supervised learning.

  • Unsupervised Machine Learning Unsupervised Learning Approach:  Compared to supervised learning approach, the unsupervised learning approach is much harder. The goal of Descriptive Machine Learning is to get the computer learn how doing something when we are not around to tell how to do it. Two major attributes used in the unsupervised learning approach. First, teaching the agent by giving some sort of reward system to facilitate success without offering any explicit categorization. Second is called ‘Clustering’, wherein the motive is not related to maximizing any utility function, but simply to find similarities in the training data.
  • Reinforcement Learning Approach:

    It is seldom used in the machine-learning paradigm. Even so, Reinforcement learning is inspired to maximize the performance of a process by letting computers and agents automatically determine specific behavior in a given context. More often, reinforcement signals in the form of simple reward feedback’s are used for agents to learn its behavior. In reinforcement learning approach, the behavior is learned from the typical environment finally or is adapted to a change in time and environment.

Three Major Examples Where Machine Learning is Successfully Used

Email Spam Filtering:

email spam recognization
Unsolicited bulk emails (UBE) also called as email spam, bulk mail or Unsolicited commercial emails (UCE) is a practice undertaken to send unwanted email messages (often containing commercial content) in large quantities to the promiscuous set of recipients. Sending spam emails are a common practice and it has become prevalent due to lower transaction cost of radical communication on the internet. White List/Black List, Bayesian Analysis, Mail Header Analysis, Keyword Matching, Postage Legislation and Content Scanning are some of the existing and renowned spam filters with an affirmative approach to identify spam messages. Although these spam filters are present, our inbox is flooded with spam messages. This happens because spammers resorting to new and advanced spamming techniques that are inflexible for these filters to adapt.

Supervised Machine Learning approach learns the features of spam emails by utilizing Naive Bayes Classifier, Multilayer Perceptron, and C 4.5 Decision Tree Classifier. The model is based on rules and the rules are framed by analyzing the mail header information, keyword matching and the body of the message. Additionally, it uses a score-based system wherein a relative score is assigned to each rule.

Image Classification and Handwritten Recognition:

Handwritten recognization

As of now, a plenty of neural network applications are presently in widespread use. Image Classification and Handwriting Recognition is one such area. Many of the machine learning tools based on neural network are capable to easily identify isolated handwritten symbols. However, these tools fail when it comes to unsegmented or connected handwriting because it is hard to gauge the beginning and end of individual letters. The best result in image classification and handwriting recognition is achieved by utilizing two dissimilar deep artificial neural networks with numerous non-linear processing stages. As of now, Multi-Column, Max-Pulling Convolutional Networks (MCMPCNN) are widely used to increase the accuracy of results. Apart from that, recurrent neural networks such as stacks of bi-directional or multi-dimensional LSTM are also used.

Face Recognition:
Increased use of smartphones has attracted a greater interest of the research community towards Automatic face analysis. There are plenty of useful applications, if a precise face detection utility is integrated into these Applications then their functionality and effectiveness can be enhanced in many folds. A system involving such an analysis assumes accurately detecting and tracking a face is easier if the facial features can be precisely identified, and the facial expressions whatsoever can be classified as well as interpreted precisely.
Given an arbitrary image, the motive behind face detection and recognition is to automatically detect and locate a human face, if it happens to be present in the given video or image.
In general, face detection and recognition are a major problem due to numerous types and n number of faces each having a different color, texture, size, etc. Face being a non-rigid object, the job is further complicated with each change in its appearance and factors such as lighting, environment, transitions, and scales.

image classification

 

Conclusion

The modern concept of Machine Learning has incorporated other data analysis approaches like pattern recognition, data mining, and few other known predictive analytic tools. Apart from using a set of variables and specified algorithms, machine learning also utilizes a generic algorithm, rule inductions, neural networks, case-based learnings, and analytical learning. These approaches can be used in a hybrid fashion to enhance the accuracy of output. The success of machine learning applications in speech recognition, computer vision, bio-surveillance and robot control is amazing. From science labs to commercial sectors and from the industrial database to e-commerce industry, machine learning is evolving to offer lucrative business solutions to enterprises and organizations.

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