Machine learning is a progressive field of computational science that has changed the regular concept of coding instead of feeding data to the generic algorithm. Based on that, it can give you a future prediction depending on the data you give it. Actually, this field is so vast that we have to write another blog on it to give a brief idea about the advantages in details. But that’s for another day. Let’s dig deep into today’s topic.
What is machine learning?
Machine learning is a field of Artificial Intelligence which allows the machine to learn from examples and experience, just like we humans do! So here we just can not talk with the machines and teach them how to handle the real-world interactions. Instead of that, we have to set some special programs which will improve their learning over time in an autonomous fashion. It compresses the need of writing a thousand lines of codes and replace it with feeding certain data.
There are so many real-life examples of machine learning which we are experiencing in our day to day life without knowing the existence of this technology. Like the weather forecasting on our smartphones, automatically tagging people on Facebook, similar product suggestions while shopping from online, or recommendations from Amazon or Netflix etc. Even, have you ever wondered when you get a call from any bank to take their privileges that you were thinking lately to give a second thought? Yes, this is another area of using machine learning where the banks can identify which could be their potential customers rather than randomly call people.
Now, you must be wondering how do these machines learn and make decisions, right? Let me tell you how-
How does machine learn?
A machine can learn from different types of data like audio, video, image etc. It can also learn from observation, interaction or experiences from an environment. We need a training dataset to create a model to train the machines by machine learning algorithm. When a new input of data is introduced to the machine learning algorithm, it makes a prediction on the basis of the model.
The prediction is evaluated for an accuracy and if the accuracy is acceptable, the machine learning algorithm is deployed. If not, the machine learning algorithm is trained again and again with an enhanced training data set.
Why machine learning is important and for which aspects?
Machine learning has diverse practical applications that drive the kind of real business results, such as time and money savings, that have the potential to dramatically impact the future of any business. At Interactions, in particular, we see a tremendous impact occurring within the customer care industry, whereby machine learning is allowing people to get things done more quickly and efficiently. As machine learning helps to predict behavior and recognizing patterns, it can be the key to unlock the value of corporate and customer data and enacting decisions that keep any business ahead of the competition. Moreover, it’s possible to handle previously unseen scenarios using machine learning. Once a machine learning model with good generalization capabilities is learned, it can handle previously unseen scenarios and take decisions accordingly.
Nowadays, most industries working with a large amount of data have recognized the value of machine learning technology. By gleaning insights from this data, often in real time, organizations are able to work more efficiently or gain an advantage over competitors. Let’s have a glance at some area where industries are benefited from this technology.
Few other potential areas where machine learning playing a vital role
- Agriculture
- Bioinformatics
- Brain-machine interfaces
- Classifying DNA sequences
- Computational anatomy
- Computer Networks
- Telecommunication
- Computer vision, object recognition
- Detecting credit-card fraud
- General game playing
- Information retrieval
- Machine learning control
- Machine perception
- Automated medical diagnosis
- Natural language processing
- Online advertising
- Search engines
- Sentiment analysis (or opinion mining)
- Software engineering
- Speech and handwriting recognition
- User behaviour analytics
- Machine translation
In my next blog, I will be discussing different types of machine learning techniques and some machine learning use cases. Till then, keep up the motivation to learn more about machine learning.
Cheers!
Thanks for this blog,
please write a blog about ANI (Artificial Narrow Intelligence) and AGI (Artificial general intelligence)
Thank you for your suggestions. We will surely look into the items.