If you ask us what is the most contemporary innovation of recent years, we would say Machine Learning without even blinking an eye. Machine Learning has helped people with improved industrial and professional procedures but has also advanced daily living because of its ability to solve complex problems effectively and quickly.
It has gained a lot of popularity in recent years and can be applied to solve some really complex problems. It also helps big data companies to unlock the true value of large data to improve IT operations.
In this article, we will discuss the real-world problems that Machine Learning can solve.
What is Machine Learning?
Machine Learning is a sub area of Artificial Intelligence and Data Science that work on data and algorithms to imitate the human brain and improve accuracy. Machine Learning is an essential part of Data Science. The ML process begins with observing data to look for patterns and make predictions for better decision making.
Some Methods of Machine Learning
Supervised Machine Learning
Supervised Machine Learning takes place when the data is labelled so as to provide correct examples for learning. Supervised learning provides excellent and near accurate results when used correctly.
- ML algorithm is provided with a smaller training dataset which is part of the bigger dataset.
- It provides an idea of the problem along with solutions.
- The ML algorithm then creates relationships between parameters.
Unsupervised Machine Learning
Unlike Supervised Learning, Unsupervised Machine Learning has no labelled data. Here, the machine randomly looks for the patterns. It does not require human labour and allows larger datasets to work. Unsupervised Learning is not as popular as Supervised Learning.
- It creates hidden structures as there is no labelled data.
- It can adapt to the data by dynamically changing hidden structures.
Reinforcement learning allows machines to automatically identify the ideal behaviour within a specific context. Here, an agent operates in an environment and has no fixed datasets. The agent has to work using the feedback.
- Here, the ML algorithm improves itself.
- Learn by trial and error to perform Better.
- Favourable outputs are reinforced and non-favourable outputs are not entertained.
Real-world problems that needs Machine learning
ML and Artificial Intelligence solutions are required in many areas and sectors including external and internal applications to help businesses achieve optimal speed and efficiency.
We generally use Machine Learning in the areas that require post-deployment improvement. These solutions have been adopted by many big companies world wide like Facebook, Twitter, Google, etc.
Let us walk you through Some Machine Learning use cases in our daily life.
Identifying spam is the most common problem solved by Machine Learning. In these times, when everythings happens over an email there is almost nobody who is unaware of the spam inbox. But, have you ever thought about how they know that a particular mail is spam? The answer is Machine Learning. ML training models can identify all the same with content-based filtering of Neural networks.
Image & Video Recognition
Image and Video recognition techniques have made tremendous progress in recent years. All thanks to Machine Learning. Image recognition techniques are used in multiple areas like object detection, face recognition, visual search, etc. while Video recognition techniques are used by companies like Salesforce, eBay, Amazon, etc.
ML algorithms can train deep learning frameworks to recognize images within datasets with much more accuracy and clarity.
Fraud detection in Banking
Fraudulent transactions are scaring everyone out there. Also every fraud transaction can be investigated keeping in mind the cost involved. Machine Learning can create predictive maintenance models to detect possible fraud activities. Fraud detection using Machine Learning can help organizations save a lot of money.
It helps in optimizing the customer satisfaction by providing them utmost protection. These fraud detection systems can create a data-based queue to investigate the incidents that require high priority.
“Ok Google” is the most common phrase these days. From Siri, Alexa to Google, voice assistance is paving its way in day-to-day life. Using them for calling people, opening emails or scheduling an appointment has become a common scene everywhere.
These virtual assistants use Machine Learning Algorithms to record our voice instructions and send them to a cloud. ML algorithm decodes these instructions and acts accordingly.
Enterprises Resource Planning
One might wonder how modern ERPs can benefit from Machine Learning. Applications of Machine Learning in ERP can help organizations through root cause analysis, customized insights, Increased production capacity, etc.
Unsupervised Learning is often used in recommendation systems. Machine Learning recognizes the products that the clients want and eventually buys. The algorithm identifies hidden patterns and works on clustering products. Many big retailers like Amazon show a list of recommended products that are based on behavioural data and parameters.
The recommended system helps businesses in generating more traffic and enhance customer engagements and boosts profit.
Machine learning can be of great use in diagnosis of any disease. It improves safety of any patient with lesser costs. It can predict the disease, extract medical knowledge, create therapy and planning, and overall patient management.
We can also use ML for analysis of medical records, like identifying irregularities, dealing with incomplete data, etc.
Leverage Ksolves’ AI and Machine Learning Consulting Services
As mentioned above, all these use cases of Machine Learning makes it the current favourite of many organizations. However, this is not the end of the road, with everyday’s advancement, the use cases will expand and it is very beneficial to be aware of the applications that can potentially utilize ML algorithms to improve efficiency. A good Machine Learning partner with right domain expertise. At Ksolves we provide effective ML solutions to manage the organizational challenges. Our team of 350+ ML experts are well-qualified to manage your ML needs and provide best suited solutions.
If you are looking for ML and Artificial Intelligence Integration services. Ksolves is an ideal solution providing budget-friendly and reliable solutions. Give us a call or write to us in the comments below.
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