Technology is improving with every passing day. Every time you read the newspaper, you will read about something new that is happening in the sector. Machine Learning is another such thing that is capturing the fancy of many technocrats and common people alike. So, what is machine learning? It is one form of artificial intelligence. This technology is used to help the machines learn through experience & data and improve their functioning ability. There is no need to explicit programming or training.

The focus of this technology is to ensure that computer programs can learn from their access to data and improve their functionality. The process of learning can include observations and data. The system will explore different patterns in data and that will help the system make better decisions in the future. There are different forms of machine learning methods. The first one is supervised which makes use of labeled examples to predict future events. On the other hand, unsupervised machine learning is making use of data that is not labeled. There is also semi-supervised which falls in between these 2 methods and makes use of both labeled and unlabeled data for training. Finally, there is reinforcement machine learning algorithm. This method of learning interacts with the environment. Here it allows the machine to automatically understand the behavior of the users and come up with actions (like rewards) which can help to maximize performance.

Where & Why is Machine Learning Used?

The next question to be addressed is about where and why machine learning is used. Here are some of the applications of this technology:

User Experience

Machine learning can significantly improve user experience. As an example Netflix and Amazon are using one form of machine learning to make personalized suggestions for users. This is based on their past usage and other elements that are discovered from their interaction with the software.

Reduced Inputs

As the system learns, the number of human inputs is reduced and that can save time. Take the example of Google Assistant. If you go to a particular commercial location regularly, it automatically detects that it can be your office. It will ask you whether that’s your work location and also provide regular traffic inputs. This way, you wouldn’t have to manually search for traffic on your route every morning. You will have it ready for you.

Security

Security is of prime concern in the modern times. Machine learning will contribute towards that. This technology will support such features as face recognition, motion detection, and object detection.

Healthcare

Machine learning can also be used to improve medicine and patient care. It will automatically understand signs and symptoms of diseases and be able to predict possibility of diseases even before they actually manifest. This could include tumor detection, drug discovery, DNA sequencing, etc.

Voice Recognition

Machine learning is being used extensively in the natural language processing segment which helps with voice recognition process.

Intelligent Decision Making

In the future, machine learning will be used extensively to make intelligent decisions. This technology will be able to rank things on the basis of their importance and make decisions on the basis of that.

Advantages of Machine Learning

Multiple Applications

There are multiple applications that can utilize machine learning technology. These could be in different industries including gaming, robot locomotion, social media, publishing, retail, healthcare & medicine, etc. Examples of healthcare include tumor detection, social media uses it for making personalized recommendation, retail will be able to use it for cross-selling & up-selling products on the basis of purchase history of the user, etc.

Personalization

Platforms can now automatically personalize their services for the users. Google & Facebook, as an example, help their advertisers by sending out personalized ads to people based on their past search behavior.

Efficiency

As mentioned earlier, machine learning will be able to reduce human interaction and minimize work which increase productivity and efficiency. Google Assistant, as an example, automatically provides inputs and reduces the amount of work you need to do.

Disadvantages of Machine Learning

Data

The first thing that needs to be pointed out is that there is a large amount of data required for machine learning. That is one of the major challenges going to be faced by machine learning technology. If the data is skewed or not available, it can have a negative effect on the performance of the technology.

Measuring Effectiveness

The second drawback is that it is hard to measure effectiveness of machine learning algorithms. Systems and rules need to be established to facilitate the same.

Nascent Stage

Finally, it is important to note that the technology is currently in its nascent stage. This means that there is more to be known about the technology and the kind of effect that it can have on our lives & efficiency of our work.

Why WeblineIndia for Machine Learning?

At WeblineIndia, we are passionate about new technologies. We have been in the industry for two decades. In this period, we have worked with numerous technologies. Our style of working involves understanding each and every technology in depth, and offering innovative solutions in the field. What also helps us in achieving this end is the hiring of high-end resources. Our team is specific to a technology, and it provides niche service in the area. As about machine learning, we have been learning a lot about this technology and have already helped some of our clients develop this technology-based application for their business.

Get in touch with us now for your all offshore outsourcing Machine learning development needs. Contact NOW!!!

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