best machine learning development company India, Ahom Technologies Private Limited

Technology has become a vital factor in our day to day life as it helps us in reducing the efforts and deliver better outcomes with maximum efficiency. Similarly, we have machine learning, a core sub area of artificial intelligence that enables computers to get into a mode of self- learning without any explicit programming. Being one of the best machine learning development companies in India, we want to make people more aware of it.

Let’s elaborate more about machine learning:

About

Machine learning is an application of artificial intelligence (AI) that makes the system capable enough to automatically learn and improve from experience without using any type of explicit programming. It mainly targets the development of computer programs that help in accessing the data which further can be used for learning about the scenarios.

Need

To better understand the needs of machine learning applications, let’s consider some of the instances where it plays an important role: self-driving Google car, cyber fraud detection, online recommendation engines like friend suggestion on Facebook, product suggestion while shopping online, Netflix showcasing the movies and shows that one might like are some of the applications of machine learning. Machines can also aid in filtering useful pieces of information that provide a helping hand in major advancements. In our day to day life, there has been a significant rise in the uses, requirements, and importance of machine learning. Due to the increased sophistication of machine learning in the last few years, Big data has become quite a buzzword as machine learning helps in analyzing some huge chunk of big data.

Uses

Some of the live examples of machine learning are:

  • Web search results.
  • Real-time ads on web pages.
  • Ads on mobile devices.
  • Email spam filtering.
  • Network intrusion detection.
  • Pattern recognition.
  • Image recognition.

Traditionally, data analysis always refers to have a character of trial and error system but this approach becomes almost impossible when the targeted data sets are huge and heterogeneous in nature. To analyze huge chunks of data, machine learning comes into the action for the solution of all such chaos by proposing extensive alternatives. With the help of fast algorithms and data-driven models for real-time processing of data, real time machine learning provides accurate results and analysis no matter how large the data is.

Machine Learning Methods

There are two main methods of machine learning which are known as supervised learning and unsupervised learning. An estimated data shows that 70 percent of machine learning is supervised learning, while unsupervised learning ranges from 10-20 percent. Methods that may often be used are semi-supervised and reinforcement learning.

Supervised Learning

A learning method in which inputs and the outputs are clearly identified and all the algorithms trained by the help of labeled examples. In this learning, the algorithm receives a set of inputs along with the corresponding correct outputs to find the errors. It is as same as to that of pattern recognition as it takes place via classification, regression, prediction, and gradient boosting. Supervised learning applications are more commonly used in those applications where we need to predict future based on the historical data.

Unsupervised Learning

Talking about unsupervised learning, it is used with data sets without any historical data. In unsupervised learning applications, the algorithm explores surpass data to find the structure and this kind of learning works best for transactional data. It also helps in recognizing the customer segments and clusters with certain attributes that are often used in content personalization.

Some of the main applications of unsupervised learning are:

  • Self-organizing maps.
  • Nearest neighbor mapping.
  • Singular value decomposition.
  • K-mean clustering.
  • Online recommendations.
  • Identification of data outliers.
  • Segment text topics.

Semi-Supervised Learning

As the name itself suggests, semi-supervised learning is a combination of both supervised and unsupervised learning that uses both labeled and unlabeled data for training or practicing. All the algorithms use a large amount of unlabeled data with a bit of labeled data. An example of semi-supervised learning includes face and voice recognition techniques.

Reinforcement learning

It is almost similar to that of the traditional type of data analysis, algorithms of reinforcement learning discover through trial and error system, based on the data it decides which action results in greater rewards. This learning functionality includes three major components named agent, environment and the actions where an agent is the learner or decision maker, the environment includes everything to which the agent interacts, and the actions are what the agent can do. Basically, this type of learning occurs when the agent chooses actions that maximize the expected reward over a given time of interval.

At present, machine learning is one of the most important technologies that include so many things we use today without even thinking about them. It provides us with extensive facilities that help in every aspect and reduces our efforts with the best outcomes based on the different scenarios. Being the best machine learning development company in India, we provide you with the most efficient solution of all your queries related to Machine Learning within the given deadline

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