Machine learning has disrupted almost every field. This branch of artificial intelligence has an outstanding potential regarding task automation and predictive capabilities which already save lives in healthcare. Machine learning is used in various cases. These are:
Machines are getting better at processing images. This type of machine learning usage is called image recognition or computer vision. It’s powered by deep learning algorithms and uses images as the input data. Facebook uses ML image recognition: it doesn’t only allow you to tag friends on photos but tags them itself.
CRM software applications can resolve the “why” questions with the help of machine learning. The models of machine learning can analyze historical data collected by the CRM apps and develop sales strategies or predict future market trends more efficiently. Also, ML is effective in churn rate reduction, customer lifetime value improvement, and companies’ competitive capabilities advancement. Data analysis, market automation, and predictive analytics are carried out by ML, which also allows companies to be available round the clock with the help of chatbots.
With the adoption of electronic health records (EHRs), medical institutions started converting patient information into a digital format and using machine learning to analyze patient data in real-time and predict possible illness complications or disease outbreaks. Besides making patient medical diagnoses more accurate, machine learning algorithms can help doctors detect breast cancer and predict its further progression rate.
With the help of ML, it is also possible to maintain inventory by taking into account the actual product demand. This demand can be predicted by analyzing the historical data. However, there are also factors that affect the demand: e.g. the day of the week, temperature, holidays, etc. Consideration of all the factors is impossible for a human, but machine learning can process voluminous data and this way predicts demand in a more precise manner. For example, IBM has analyzed an enormous database of The Weather Company and found out that yogurt sales are higher when the wind is above average, and autogas sales increase during the colder than average temperatures.
Speech recognition is the process of translating spoken words into text for the machine to understand. Of course, this process is based on machine learning models, which analyze tons of speech patterns to recognize what has been spoken. A specific software application can recognize the language spoken in audio, and convert this audio into text. For instance, Google Assistant, Siri, Alexa, and other speech recognition devices. In applications, speech recognition is used like a voice user interface, voice searches, etc. Voice UIs include voice dialing, call routing, and appliance control. They are also used for data entry in a simple way and the preparation of structured documents.
In finance, arbitrage belongs to short-term automated trading strategies that have a large number of securities. In these strategies, the user focuses on implementing the trading algorithm for a set of securities based on historical correlations and the general economic variables. Machine learning is applied to statistical arbitrage to obtain an index arbitrage strategy. Linear regression and the Support Vector Machine are applied to the prices of stocks streams.
Learning associations is the process of developing knowledge about how unrelated products can be associated. Here, machine learning is applicable to study the associations between the products people buy. If a person buys a product from a certain category, he/she will be shown similar products out of relational aspects between the two products. New products launched on the market have to increase their sales and, for this reason, are associated with the old ones.
Classification is the process of placing individuals into their specific classes. This process is helpful in analyzing the measurements of an object to identify the category it belongs to. Efficient relations are defined through data. For example, before a bank distributes loans, it assesses the customers’ ability to pay loans (earnings, savings, and financial history) based on the customers’ previous experience with loans.
Machine learning is used in predicting. In loan distribution, to define the probability of a fault, the system needs to classify the gathered data into groups. Data analysts set rules and after the classification is done, the probability of the fault is calculated. These computations are applicable to any industry and different purposes.
Extraction of information is the process of extracting structured information from unstructured data with the help of ML. The process of extraction involves a set of documents as input (web pages, articles, blogs, business reports, and emails) and gives out the structured data.
The principle of machine learning is used to optimize the parameters in regression. ML can be also used to decrease the approximation error and calculate the closest possible outcome, for function optimization, to alter input to get the closest possible outcome.
Machine learning is the driving force behind the popularity of financial services. Machine learning can help banks and other financial institutions to