As you probably know, machine learning studies computer algorithms to learn how 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 examples, 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. In this article, we will see various applications of machine learning.

Image Recognition

One of the most common uses of machine learning is image recognition. There are many situations in which you can classify the object as a digital image. For digital images, the measurements describe the outputs of each pixel in the image.

In the case of a black and white image, the intensity of each pixel serves as one measurement. So, if a black and white image has N*N 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.

: 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 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 ten 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.

Although the details of signal representation are outside the scope of this article, we can represent the signal by a set of real values.

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

Medical Diagnosis

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

It is argued that the successful implementation of ML methods can help the integration of computer-based systems in the healthcare environment providing opportunities to facilitate and enhance the work of medical experts and ultimately to improve the efficiency and quality of medical care.

In medical diagnosis, the main interest is in 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 improves the accuracy of medical diagnosis by analyzing data of patients.

The measurements in this application are typically the results of certain medical tests (i.e. blood pressure, temperature, blood tests), medical diagnostics (such as medical images), the presence/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 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.)

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.

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.

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, the seeker uses this data to create a relationship between customer attributes and related risks.

Prediction

Consider the example of a bank computing the probability of a loan applicant 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 needs, we can compute the probability. These probability computations can compute across all sectors for varied purposes

The 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 reports from last month/year/5 years/Diwali/Christmas/etc. This type of reporting is called historical reporting. But currently, business is more interested in finding out what their sales will be next month/year/Diwali/etc. This way, businesses can make required decisions (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 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.

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.

Conclusion

In conclusion, machine learning is an incredible breakthrough in the field of artificial intelligence. While it does have some frightening implications when you think about it, the machine learning applications listed here are just some of the many ways this technology can improve our lives.