The Ease of Data Science and Machine Learning
Intro: We can not but always appreciate the ease Data Science and Machine Learning have made possible. Application of Data Science and Machine Learning has aided many companies in delivering better services to their customers and hence increase in profit. To take advantage of data science and machine learning in these days will not only bring about sales but quality service, which is a key factor in spreading the fame of any firm.
Below are a list of applications of machine learning and companies that have utilized data science and machine learning
Image Recognition
This is no longer a new thing as many companies have employed the use of this tool in their various products and services. Image recognition is a vital too today and is mostly important for cyber security. Companies who are actively using this are: Google, Facebook, Apple, Helping Faceless, Amazon, ResolutioonView amongst others.
Speech Recognition
This another great invention from ml and it has aided many vital solutions provided by great companies like Baidu, Hound, Apple(Siri), Microsoft(Cortana), Google(Google Assistant), Amazon(Alexa) to mention a few.
Medical Research
In medical research we have couple of applications that has yielded breakthrough in the past few years and these include:
- Chatbots: Companies are using AI-chatbots with speech recognition capability to identify patterns in patient symptoms to form a potential diagnosis, prevent disease and/or recommend an appropriate course of action. Good examples are Babylon Health in UK, Ada Health in Berlin
- Oncology: Researchers are using deep learning to train algorithms to recognize cancerous tissue at a level comparable to trained physicians. Stanford University researchers have been able to successfully train an algorithm to diagnose skin cancer using deep learning, specifically deep convolutional neural networks (CNNs)
- Pathology: Pathology is the medical specialty that is concerned with the diagnosis of disease based on the laboratory analysis of bodily fluids such as blood and urine, as well as tissues. Machine vision and other machine learning technologies can enhance the efforts traditionally left only to pathologists with microscopes. A team of researchers from Beth Israel Deaconess Medical Centre and Harvard Medical School have used deep learning to train an algorithm that integrates multiple speech recognition and image recognition to diagnose tumors.
- Rare Diseases: Facial recognition software is being combined with machine learning to help clinicians diagnose rare diseases. Patient photos are analyzed using facial analysis and deep learning to detect phenotypes that correlate with rare genetic diseases. The Face2Gene app is a facial recognition software is being combined with machine learning to help clinicians diagnose rare diseases (in this case, from facial dysmorphic features). Patient photos are critically analyzed using facial analysis and deep learning to detect phenotypes that correlate with rare genetic diseases.
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.
Regression
We can also apply Machine learning to regression. Let’s assume that x= x1, x2, x3, … xn are the input variables and y is the outcome variable. Here 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 the parameters. To cut the approximation error and calculate the closest possible outcome.
There are many other applications of data science and machine learning, which include: extraction( Information Extraction (IE) is another application of machine learning. It is the process of extracting structured information from unstructured data), prediction, classification, even learning association( this 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.) to mention a few.
