Applications Of Machine Learning
In present, Artificial Intelligent (AI) has changed significantly. In that result Machine Learning born on based in AI. So, machine learning construction of artificial intelligence capable of learning new data which isn’t programmed it. Machine Learning is a subset of the science of data mining.
The intelligent systems built on machine learning algorithms have the capability to learn from past experience or historical data. In this article, I will discuss a few examples of machine learning use for applications in real time scenarios. from the Financial, Marketing, Health, and Safety it can be used to identify Geographical problems etc.
Machine learning is very useful in any application that’s based on pattern recognition. This includes obvious applications like facial recognition software and handwriting recognition.
Pattern Recognition, what is this????
Pattern recognition is the automated recognition of patterns and regularities in data. Pattern recognition is closely related to artificial intelligence and machine learning, together with applications such as data mining and knowledge discovery in databases (KDD), and is often used interchangeably with these terms.
— Wikipedia —
In my words, pattern recognition can take the form of any kind of visual, audio or numerical data, depending on the intended use of the software. These are some examples of the popular machine learning competition hosting sites include:
- kaggle — (https://www.kaggle.com/)
- NUMERAI — (https://numer.ai/)
- Topcoder — (https://www.topcoder.com/)
- CrowdAnalytics — (https://www.crowdanalytix.com/)
- DrivenData — (https://www.drivendata.org/)
How machine learning is helping in creating better technology to power today’s ideas?
Facial recognition
Facial recognition is one of the most common uses of machine learning. There are many situations for using facial recognition. As an example, High-quality cameras in mobile devices have made facial recognition a viable option for authentication as well as identification. Apple’s iPhone X is one of example to it. Facial recognition application works in the software identifies 80 nodal points on a human face. And nodal points are endpoints used to measure variables of a person’s face, such as the length or width of the nose, the depth of the eye sockets and the shape of the cheekbones.
Machine Learning can be used for image recognition as well.
Voice Recognition
Voice recognition in other word speech recognition is voice/spoke convert to the text. It is also known as automatic speech recognition (ASR). We can be looking for examples that google assistant, Cortana, Siri, and Alexa. Voice recognition is one of the categories in deep learning. The system analyzes the human-specific voice and uses it to fine-tune the recognition of that person’s speech, resulting in increased accuracy. Simple voice commands convert to decode and may be used to initiate phone calls, select radio stations or play music from a compatible smartphone, MP3 player or music-loaded flash drive.
For an example, use your smart device and open google assistant (android)or Siri(IOS) and say “Hello Google/Siri!” and the device responds to you. It is by measuring the sounds a user makes while speaking, voice recognition software can measure the unique biological factors that, combined, produce her/his voice.
Financial Services
Machine Learning has a lot of potential in the financial and banking sectors. It helps the banks financial institution.
For example, Taaffeite Capital Management (http://taaffeitecm.com/). Taaffeite Capital trades in a fully systematic and automated fashion using proprietary machine learning systems. Here is a list of funds and trading firms that are using machine learning.
Designing new products and service offering to right customers by appropriate data mining that supports easy, flexible and integrated processes for understanding customer buying habits, and then which channels customer engages with, and what are key influencing factors are is very critical for banks to sell. Applying machine learning to produce personalized product offering is key for next-generation banking.
Identifying a risk score of a customer based on his nationality, occupation, salary range, experience, an industry he works for, credit history etc. It is very critical for banks before even offering a product or service to the customer. This risk score is an important KPI for banks to decide on interest rate and other product behaviors for the customer.
Health Care
There are many manual processes in the health care industry. In this area technology always helps to improve the understand patient’s situation. Using these types of advanced analytics, we can provide better information to doctors at the point of patient care. Having easy and quick to access the patient’s blood pressure, Heart rate, Lab testing, DNA testing etc.
There is also great values to be gained from machine learning at the clinical trial stage of the medical procedure.
Conclusion
We discussed how machine learning can combine with real-time applications. Machine learning is changing in a day to day life and improve the technology based on AI, ML and Deep learning such as. Now we know about there’s a lot that machine learning can do, but I mention in my article only a few applications and most of us are already experiencing the rise of this technology without realizing it! The several applications of machine learning in various industries is very evident. But in future, machine learning can be ascertained only with time.
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