Jyoti Nigania

Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree. ...

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Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.

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Machine Learning the Real World Applications

By Jyoti Nigania |Email | Mar 15, 2018 | 5109 Views

As everyone knows Machine learning studies computer algorithms for learning to do stuff. We might for instance, be interested in learning to complete a task, or to make accurate predictions, or to behave intelligently. The learning that is being done is always based on some sort of observations or data, such as direct experience, or instruction. So in general, machine learning is about learning to do better in the future based on what was experienced in the past.
  Machine learning is being used in a lot of real-world applications for various purposes. Various applications of Machine Learning
Image Recognition
One of the most common uses of machine learning is image recognition. There are many situations where you can classify the object as a digital image. For digital images, the measurements describe the outputs of each pixel in the image.

  • For face detection:  The categories might be face versus no face present. There might be a separate category for each person in a database of several individuals.

  • For character recognition: We can segment a piece of writing into smaller images, each containing a single character. The categories might consist of the 26 letters of the English alphabet, the 10 digits, and some special characters.

Speech Recognition
Speech recognition (SR) is the translation of spoken words into text. It is also known as "automatic speech recognition" (ASR), "computer speech recognition", or "speech to text" (STT).

In speech recognition, a software application recognizes spoken words. The measurements in this application might be a set of numbers that represent the speech signal. We can segment the signal into portions that contain distinct words or phonemes. In each segment, we can represent the speech signal by the intensities or energy in different time-frequency bands.

Speech recognition applications include voice user interfaces. Voice user interfaces are such as voice dialing; call routing, demotic appliance control. It can also use as simple data entry, preparation of structured documents, speech-to-text processing, and plane.

Medical Diagnosis

Machine Learning provides methods, techniques, and tools that can help solving diagnostic and prognostic problems in a variety of medical domains. It is being used for the analysis of the importance of clinical parameters and of their combinations for prognosis, e.g. prediction of disease progression, for the extraction of medical knowledge for outcomes research, for therapy planning and support, and for overall patient management. Machine Learning is also being used for data analysis, such as detection of regularities in the data by appropriately dealing with imperfect data, interpretation of continuous data used in the Intensive Care Unit, and for intelligent alarming resulting in effective and efficient monitoring.

The measurements in this application are typically the results of certain medical tests such as blood pressure, temperature and various blood tests or medical diagnostics such as medical images, presence or absence intensity of various symptoms and basic physical information about the patient age, sex, weight etc. On the basis of the results of these measurements, the doctors narrow down on the disease inflicting the patient.

Statistical Arbitrage

In finance, statistical arbitrage refers to automated trading strategies that are typical of a short-term and involve a large number of securities. In such strategies, the user tries to implement a trading algorithm for a set of securities on the basis of quantities such as historical correlations and general economic variables. These measurements can be cast as a classification or estimation problem. The basic assumption is that prices will move towards a historical average.

Learning Associations

Learning association is the process of developing insights into various associations between products. A good example is how seemingly unrelated products may reveal an association to one another. When analyzed in relation to buying behaviors of customers.

One application of machine learning often studying the association between the products people buy, which is also known as basket analysis. If a buyer buys 'X', would he or she force to buy 'Y' because of a relationship that can identify between them. This leads to a relationship that exists between fish and chips etc. when new products launch in the market a Knowing these relationships it develops a new relationship. Knowing these relationships could help in suggesting the associated product to the customer. For a higher likelihood of the customer buying it, it can also help in bundling products for a better package.

This learning of associations between products by a machine is learning associations. Once we found an association by examining a large amount of sales data, Big Data analysts. It can develop a rule to derive a probability test in learning a conditional probability.

Classification

Classification is a process of placing each individual from the population under study in many classes. This is identified as independent variables.
Classification helps analysts to use measurements of an object to identify the category to which that object belong. To establish an efficient rule, analysts use data. Data consists of many examples of objects with their correct classification.

For example, before a bank decides to disburse a loan, it assesses customers on their ability to repay the loan. By considering factors such as customer's earning, age, savings and financial history we can do it. This information is taken from the past data of the loan. Hence, Seeker uses to create a relationship between customer attributes and related risks.

 Prediction

Consider the example of a bank computing the probability of any of loan applicants faulting the loan repayment. To compute the probability of the fault, the system will first need to classify the available data in certain groups. It is described by a set of rules prescribed by the analysts.

Once we do the classification, as per need we can compute the probability. These probability computations can compute across all sectors for varied purposes

Current prediction is one of the hottest machine learning algorithms. Let's take an example of retail, earlier we were able to get insights like sales report last month / year / 5-years / Diwali / Christmas. This type of reporting is called as historical reporting. But currently business is more interested in finding out what will be my sales next month / year / Diwali, etc.So that business can take required decision (related to procurement, stocks, etc.) on time.

Extraction

Information Extraction (IE) is another application of machine learning. It is the process of extracting structured information from unstructured data. For example web pages, articles, blogs, business reports, and e-mails. The relational database maintains the output produced by the information extraction. The process of extraction takes input as a set of documents and produces a structured data. This output is in summarized form such as excel sheet and table in a relational database.

Conclusion

Machine learning is an incredible breakthrough in the field of artificial intelligence. This is helpful in all aspects and in daily use as well. These Machine Learning Applications are several of the many ways this technology can improve our lives.

Source: HOB