Machine Learning

Machine learning is a subset of artificial intelligence which automates analytical model building. The idea behind machine learning is that the machine can learn from data, recognize patterns, and make decisions by their own with limited human intervention.

Evolution of Machine Learning

With rapid advancements in computer technology, machine learning is also growing significantly. Inception of machine learning was started from pattern recognition and the idea that machines are able to learn without being programmed. Significant research and development activities are also taking place in the field of machine learning.

In machine learning model, iterative aspect is important as whenever any new data is exposed, the model adapt it and update it automatically. It learns from earlier computations to generate reliable and iterative decisions. The model is gaining momentum rapidly.

Several machine learning algorithms have been around for a long time. However, one of the recent advancements in this field include successfully applying complex mathematical calculations to big data repeatedly and effectively.

Why is Machine Learning Important?

A rapid upsurge in machine learning has been registered owing to the same factors which made Bayesian analysis and data mining more popular than ever. Some of the factors include huge volume of data, affordable data storage, and availability of cheaper computational processing. All of these factors prove that it’s possible to generate models which are able to analyze huge and complex data and deliver quick and accurate results. This also increase the profitability of organization by minimizing unwanted risks.

Machine Learning Algorithm

The machine learning methods are categorized in different segments, in which the most widely adopted models are unsupervised learning and supervised learning. A brief overview of all the models are stated below.

Unsupervised learning 

These algorithms have applications where no historical labels are available. By using this model, the user anticipates to find some structure by exploring the data sets. These models are used for transactional data. For instance, it can categorize the customer’s segment with similar attributes or it can be used for finding main attributed which separates the customers from each other. Also, these models are used to identify data outliners, recommended items, and text topics. Some of the common techniques used in this model include singular value decomposition, self-organizing maps, k-means clustering, and nearest-neighbor mapping.

Supervised Learning

Supervised learning algorithms ate trained using labeled examples where the required output is already known. A set of inputs along with outputs is being inserted and the algorithm compares its actual output with calculated outputs to find errors. Then, it modifies the algorithm accordingly. The supervised learning uses different methods including gradient boosting, classification, prediction, and regression in order to predict the label values on additional unlabeled data. These algorithms are majorly used where the future data is predicted by using historical data and events. For instance, these models can estimate that which insurance customer is likely to file a claim.

Semisupervised learning 

Semisupervised learning has similar applications as supervised learning. Although, it uses both unlabeled and labeled data for training. Typically, it uses large amount of unlabeled data and small amount of labeled data. This is mainly due to effortless acquisition and cost-effective nature of unlabeled data. This model is used with methods such as prediction, regression, and classification. These models are useful where the labelling cost is too high to afford for training purposes. Early examples of this model is face identification on a web cam.

Reinforcement learning 

Reinforcement learning has major application in avigation, gaming, and robotics. The basic principal behind this algorithm is discovering through trial and error. The training model has three primary components:

  • The agent: The learner or decision maker
  • The environment: It includes everything the agent interacts with
  • The actions: It includes the actions the agent can perform
Machine Learning Applications

The technology is gaining popularity among most of the industries where a large volume of data is being used. By gathering real time insights from this data, enterprises are able to gain competitive advantage and work more effectively. Some of the major end-users for machine learning technology is as follows

  • Transportation
  • Financial services
  • Oil & Gas
  • Government
  • Retail
  • Healthcare

Transportation

The transportation industry relies on ensuring more efficient routes and predicting hurdles in the upcoming route in order to increase profitability. Hence, analyzing data to identify trends and patterns is an essential element in this sector. The modeling and data analysis aspects of machine learning algorithms are necessary to public transportation, delivery companies, and other logistics companies.

Financial services

Financial companies and banks use machine learning technology to gather insights from data sets and prevent the system from fraud. Also, the technology helps in identify investment opportunities and suggesting the investors the best time to trade. Also, it enables cybersurveillance to identify high risk profiles and pop up warning alerts of vulnerabilities.

Government

Government agencies consists of several data sources which needs to be mined for insights. It necessitated the requirement for machine learning technology in public safety and utilities. The government agencies consist of confidential and sensitive data which needs to be protected from fraud and other unwanted activities. 

Health care

The healthcare industry has shown significant adoption for machine learning technology owing to introduction of sensors and wearable devices. Some devices use large volume of data to access patent’s health in real time which improve the treatment efficiency and reliability. Also, it helps the healthcare professionals in identifying red flags and trends which may result in improving treatment and diagnoses.

Retail

Machine learning analyzes buying or surfing history of consumers and show customized recommendations to specific customers. Retailers depend on machine learning to provide personalized shopping experience, merchandise supply planning, design marketing campaign, and price optimization. This is expected to improve the user experience and help retailers in expanding their customer base.

Oil and gas

In oil & gas industry, machine learning is used for various purposes including streamlining oil distribution, finding new energy sources, predicting refinery sensor failure, analyzing minerals in ground, and several other purposes. The technology is widely being used in oil & gas sector and the adoption rate is still expanding.

What would be the future scope of machine learning programs? Let me know your insights in comment box 🙂

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