Hire Machine Learning Experts in India

What is Machine Learning?

Machine Learning (ML) is a type of artificial intelligence that allows computers to learn from data, identify patterns and make predictions. Machine Learning algorithms are used in a variety of applications, such as email filtering and computer vision.

In the business world, Machine Learning is often used for predictive analytics and customer segmentation.

For example, ML can be used to predict which customers are likely to churn or which products a customer is likely to buy. Machine Learning can also be used to segment customers into groups based on their similarities. This information can then be used to provide each group with customized content and offers.

As per a report, the ML market size is projected to grow by $16.7 billion by 2027, at a CAGR of 39.25%.

What are the benefits of using Machine Learning?

There are many benefits of using Machine Learning, some of which include:

Improved accuracy – Since ML algorithms are based on a set of rules that are adjusted using data, they can often achieve high levels of accuracy much faster than alternative methods.

For example, Google Translate uses neural networks and other Machine Learning algorithms to translate languages with a high degree of accuracy. This means that when you type or speak into your phone, it can recognize what language you’re speaking and provide you with an instant translation.

Increased convenience – Rather than taking hours or days to produce a result, an ML algorithm can often be adjusted and re-trained in minutes. This means that it can be incorporated into almost any part of your business workflow, whether you’re using it to select products for your eCommerce site or customize ads for individual users.

Improving speed – Many businesses are now using Machine Learning algorithms to adjust and improve their workflows, helping them save time and money.

Google, for instance, uses Machine Learning algorithms to automatically filter offensive content in its search results. This means that while humans review 100% of search results, they don’t have to spend hours shifting through content that is irrelevant or inappropriate.

Why should I hire Machine Learning Developers from India?

There are various reasons to hire Machine Learning Developers from India; some of them are listed below:

Cost-Effective – The cost of hiring a competent developer in India is significantly lower compared to other countries such as USA and Australia. Also, there are many free tools available for Machine Learning development that saves firms money.

Transparent Market – A transparent market allows you to find and choose your desired developer without any hassle. Further, it also allows you to have a clear understanding of what exactly are you getting into before making any decisions.

Top Quality Work – Hiring ML developers from India guarantees you top quality work. The market is filled with firms providing quality services. Hence, they hold a good reputation in front of their clients and do not settle for anything less than excellence.

Accommodating Timeline – Hiring an Indian developer allows you to have a convenient timeline. There are many developers working for 12 hours and more in India which enables them to deliver projects within your given time frame.

Fast Turnaround – On-boarding with a developer from India gives you complete control over the delivery time of your project. The process of development, testing and bug fixing is usually faster as there is no need for paperwork that needs proper approvals like in the case of offshore companies.

What are the types of Machine Learning?

There are four main types of ML:

Supervised learning – Supervised learning algorithms are trained using labelled data. The labels are provided by humans. For example, a supervised learning algorithm could be used to classify images of animals like cats or dogs.

Unsupervised learning – Unsupervised learning algorithms are trained using data that is not labelled. The algorithm must learn to identify patterns on its own. For example, an unsupervised learning algorithm could be used to cluster customers into groups based on their similarities.

Semi-supervised learning – Semi-supervised learning algorithms are trained using both labelled and unlabeled data. This is helpful when there is not enough labelled data to train a supervised learning algorithm.

Reinforcement learning – Reinforcement learning algorithms are trained using a reinforcement signal. This signal is used to reinforce or punish the algorithm for its predictions. For example, a reinforcement learning algorithm could be used to control a robot arm. The arm would be rewarded for picking up objects and punished for dropping them.