What is Machine Learning and it’s Future

Machine learning can be defined as Artificial Intelligence. With the help of technology and human knowledge, certain decisions and tasks can be automated with this process. With the help of Machine Learning, it will give an opportunity for computers or robots to assist or handle new situations via analysis, self-training sessions, experience and various observations.

Machine learning is a continuous process where the data is analyzed in different situations and also exposure towards new scenarios, testing etc. With the help of this repetitive actions, the machines will be able to take appropriate decisions whenever it is required.

Machine learning is different when compared to Data mining and Knowledge discovery in databases, which is abbreviated as KDD.

Some examples of Machine Learning and its utilization:

1. With the help of technology advancements, machine learning was not the same as we had in the past. All the algorithms and the analysis is happening at a faster rate. The most interesting topic for the automobile industry is self-driving cars: “Google Cars”.
2. Machine learning applications are so wide spread. The ad’s that you see while browsing is based on your recent searches. This is nothing but machine learning.
3. Fraud detection is another key area where the machine learning applications are more widely used.

So what is the fuss about machine learning, why there is increased interest in machine learning nowadays?

Well, machine learning has been a hot topic for the year 2017 and so far we have seen a lot of companies have started investing in self-driven cars and trying to automate the transport industry with this.
The following activities or processes are already using the benefits of Machine learning but a lot has to improve and this would be seen in next coming few years and it is to be expected as the major focus has been allocated on this concept.
1. This is because of Technology advancement. A huge amount of data in various forms are available where the analyzing models are built more robust and the data storage got wider and cheaper compared to the past. All these factors get together provide a better environment where all the data is analyzed and gets to a stage where it is helping the individual or helping the organization in terms of better decision making and being very specific in terms of results prediction. So all of this is happening without human intervention, which is great.
2. Machine learning is all about analyzing the data based on the data models and the humans can design and define only a few models per week but when it is done electronically it is limitless.
3. The increased expectation of being accurate and being more productive in less time is something that is pushing mankind to a different stage where they are relying more on the machines to do the heavy lifting and rest leave the output to the individuals or the team who is monitoring.

So how is machine learning is utilized today in our daily life?

The use of machine learning is implemented across the world and it is not limited to the following:
Fraud detection:
Based on your buying pattern if you the system observes any uncertainty it quickly alerts the authorities and at the same time intimates the user.
The best example is your Gmail account login. If you have logged into your Gmail account from a different city or country or logged into your Gmail account from a new device, immediately you will get any email to your account. This means your regular device and the internet connection is been logged somewhere and a system is monitoring for uncertainties.
Web search results:
All your search results are tracked and based on your search history pattern, real time ads are displayed on the web browsers.
For example: If you have searched for any Televisions on Amazon and closed your browser and then started browsing or reading something over the internet, TV related ads will be shown in the browser prompting the user to buy.
Text based sentiment analysis:
The best example for this is your Gmail app on your iPhone or your Android phone. If you have the latest app updated on your phone, whenever a user reads the email, predefined options are displayed for the user to reply back. This way the user doesn’t have to manually type. This is one such example where the email is read by the system already and it has come to certain conclusions and the keywords are displayed for the user.
A lot of software companies have built a product using text based sentiment analysis concept and minting money on these products. Once the product is utilized by companies they can understand their customer’s reviews and comments. All the comments and reviews put forward by their customers are read and analyzed and categorized into meaningful data for the business. With this, they can classify if their customers are satisfied or if they have a problem the business would understand how many individuals have faced this particular problem.
Thus helping the business in every aspect and helping them to correct themselves and maintain a profitable business for themselves.
Prediction of Equipment Failures:
This is more widely utilized in developed countries. For example, we have transformers which are prone to wear and tear because it is directly exposed towards sunlight. So understanding their life span and their maintenance activities, the machine learning systems are capable enough to predict what it is the life expectancy of this equipment. This should be more widely used in all the sectors so that it will be helpful for the businesses.
Pattern and Image recognition:
Artificial Intelligence has played a vital role in terms of facial recognition. Using the capability a lot of security systems have been built and border crossing has been more clear and monitored.
The best example is London Airport it is equipped with one of the best facial recognition setups which will help the border officers to take appropriate actions if a criminal is detected.


  1. Very nice article and useful for all. Thank You

  2. Thanks, very helpful information.

  3. Nice to read, thank you!

  4. Greetings!

    I really appreciate this kind of contribution.

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