shiwaneeg

I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots. ...

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I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots.

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Practical Implications of Machine Learning

By shiwaneeg |Email | Apr 20, 2018 | 11907 Views

Machine learning (ML) is an incredible innovation in the field of AI. It provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. It majorly focuses on the development of computer programs that can access data and use it to learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for data patterns and make better decisions in the future based on the examples that we provide. The primary aim of ML is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.

Heard a lot about machine learning? Have you ever thought about its practical implications?

This article will mention some of the applications of Machine Learning in our lives & practices:

i. Image Recognition:

The common use of ML is in image recognition. There are many situations in which you can classify the object as a digital image. The measurements describe the outputs of each pixel in the image.

In the case of a black & white image, the intensity of each pixel serves as one measurement. So, if a black and white image has NxN pixels, the total number of pixels and hence measurement is N2.

In the colored image, each pixel is considered as providing three measurements of the intensities of three main color components, i.e. RGB. So in the N*N colored image, there are three N2 measurements.
  • Face detection: The category might be face present vs. no face present. There might be a separate category for each person in a database of several individuals.

  • Character recognition: We can section a piece of writing into smaller images, each containing a single character. The categories might consist of all the English alphabetical letters, numeric digits, and some special characters.
ii. Speech Recognition

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

Speech recognition applications include voice user interfaces. Voice user interfaces include voice dialing, call routing, and easy home appliance control. It can also be used for simple data entry, preparation of structured documents, speech-to-text processing, and planes.

iii. Medical Diagnosis:

ML provides methods, techniques, and tools that can help to solve diagnostic and prognostic problems in a variety of medical areas. It is being used to analyze important clinical parameters and their combinations for prognosis, i.e. prediction of disease progression, extraction of medical knowledge for research, therapy planning, support, and overall patient management. 

ML is also used for data analysis, such as detection of data regularities by appropriately dealing with imperfect data, for interpreting continuous data used in the ICU, and for intelligent alarming, which results in effective and efficient monitoring.

The successful implementation of ML methods can help in the combination of computer-based systems in the healthcare environment. It provides opportunities to facilitate and enhance the work of medical experts. It also facilitates in improving the efficiency and quality of medical care.

In medical diagnosis, the key area to focus upon is establishing the existence of a disease followed by its accurate identification. There is a separate category for each disease under consideration and one category for cases in which no disease is present. Here, machine learning facilitates to improve the accuracy of medical diagnosis by analyzing data of patients.

The measurements in this application are typically on certain medical tests such as blood pressure, temperature, blood tests, medical diagnostics, the presence/absence/intensity of various symptoms, and basic physical information about the patient. On the basis of these measurements, the doctors focus thoroughly on the disease affecting the patient.

iv. Statistical Arbitrage:

In finance, statistical arbitrage refers to automated trading strategies that are typical for the 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 based on 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 toward a historical average.

We apply machine learning methods to obtain an index arbitrage strategy. In particular, we employ linear regression and support vector regression (SVR) onto the prices of an exchange-traded fund and a stream of stocks. By using principal component analysis (PCA) in reducing the dimension of feature space, we observe benefits and note the issues in the application of SVR. To generate trading signals, we model the residuals from the previous regression as a mean reverting process.

In the case of classification, the categories might be sold, bought, or not touched for each security. In the case of estimation, one might try to predict the expected return of each security over a period of time. In this case, one typically needs to use the estimates of the expected return to make a trading decision (buy, sell, etc.)

v. 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 the buying behaviors of customers.

This application of machine learning involves studying the association between the products people buy and is also known as basket analysis. If a buyer buys X, would they buy Y because of a relationship that can be identified between them? Knowing these relationships could help in suggesting an 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 called learning associations. Once we find an association by examining a large amount of sales data, big data analysts can develop a rule to derive a probability test in learning a conditional probability.

vi. Classification:

Classification is the process of placing each individual from the population under study in many classes. This is identified as independent variables.

Classification helps analysts use measurements of an object to identify the category to which that object belongs. To establish an efficient rule, analysts use data. Data consists of many examples of objects with their correct classification.

vii. Extraction:

Information extraction (IE) is another application of machine learning. It is the process of extracting structured information from unstructured data such as, 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 structured data. This output is in a summarized form such as an Excel sheet or a table in a relational database.
Extraction has become key in the big data industry.

As we know, a huge volume of data is generated all the time, and most of this data is unstructured. The first key challenge is handling unstructured data. Now, the conversion of unstructured data to structured form based on some pattern so that the same can stored in RDBMS.

Apart from this, in current days, data collection mechanisms are also changing. Previously, we collected data in batches like end-of-day (EOD), but now, business want the data as soon as it is getting generated, in real-time.

viii. Regression:

We can apply machine learning to regression, as well.

Assume that x= x1, x2, x3, ... xn are the input variables and y is the outcome variable. In this case, we can use machine learning technology to produce the output (y) on the basis of the input variables (x). You can use a model to express the relationship between various parameters as below:

Y=g(x) where g is a function that depends on specific characteristics of the model.

In regression, we can use the principle of machine learning to optimize parameters and to cut the approximation error and calculate the closest possible outcome.

We can also use machine learning for function optimization. We can choose to alter the inputs to get a better model. This gives a new and improved model to work with. This is known as response surface design.

Machine learning is a commendable innovation in the field of artificial intelligence. Machine learning applications can transform our lives drastically. And, to get most out of machine learning, we have to know how to pair the best algorithms with the right tools and processes.

Source: HOB