Machine learning ML is also an important type of artificial intelligence AI and predicting the outcomes becomes very easy as it allows the software application to become more accurate without any explicit program done. It is a process which enables the data analysis to automate the analytic model building. It is basically made on the artificial intelligence idea which says that the system learns from the data, can easily identify the patterns and itself can make all the decisions without the involvement of human beings. So, the general work of the machine learning is to build such algorithms that predict the right output by using statistical analysis by taking the right input in an acceptable range.

Whereas machine language is a trade within the computer language which it is still different from traditional computer techniques. In traditional computing techniques, the algorithms used are explicitly programmed and certain instructions are given to the computer to calculate or solve a problem. Instead of machine learning, the algorithms trains the computers on the correct input of the data and get the output in a statistical order which falls within that specific range. This allows the machine language to build models from the sample data provided and which automates the process of taking decision-making by the data provided.

Everyone who is using this advanced machine language has been benefited from it. The technology like the face recognition which helps the users to tag, share and post their pictures on the social media platforms. Another technology that is the OCR or optical character recognition that makes a normal image of the movable type. Whatever T.V shows or movies we watch are based on the user-preferences which were done by the recommendation engine in machine learning of artificial intelligence.

As machine learning is a part of artificial intelligence so a course on machine learning is always a great idea as it is the most developing field nowadays. While working with the methodologies of machine learning you to keep a lot of consideration in mind and should know its importance and analyze it properly.

MACHINE LEARNING TECHNOLOGY EVOLUTION

As the technologies get improved and get advanced similarly the previous machine learning is not same as the new machine technologies. It was basically developed from the technology called pattern recognition and the based on the theory that a specific task can be performed without being programmed in the computer: the most important challenge for the researchers was to see if it the computer could learn effectively from the data already provided. In machine learning, as the models are always get exposed to new data, they have adapted themselves independently moreover machine learning is an iterative process. From every growing time, they adapt themselves easily they produce reliable, repeated decision and results. With every new machine learning something brilliant is coming up which is gaining the fresh momentum.

There are a lot of machine learning algorithm that has been invented till now, having the ability to apply mathematical calculation complex that is automatically added to the Bigdata which are faster and faster and over and over is the development done recently. Below are some of the examples which include the machine learning technique:

  • The self-driving Google cars include machine learning techniques.
  • Amazon Prime and Netflix online recommendation offers.
  • Checking what people say about you on twitter.
  • One of the most important aspects is the detection of fraud.

DIFFERENT MACHINE LEARNING METHODS

The most adopted method in machine learning method is the Supervised learning and the Unsupervised learning instead of this there are some other are methods also that are listed below:

  1. Supervised learning: using labeled examples the algorithms are being trained where the input is given the desired output is already known. Considering an example there is an equipment which has data points labeled as F for failed and R for the run. A set of input would be received by the learning algorithm with the corresponding set of outputs, then it compares itself with the existing output and the real desired output this is how the algorithms learn about the errors. After knowing the errors it modifies the model accordingly. There are some other methods also like classification, regression, prediction, and gradient boosting instead the supervised learning is used as the values can be predicted using patterns on the label on the additional unlabeled data. The applications where the future events can be determined by the previous data are commonly learned from supervised learning. For example, it can easily determine when the transaction on the credit card is going to be fraudulent or which customer of the insurance is going to file the complaint.
  2. Unsupervised learning: the data which doesn’t have any historical or previous label then unsupervised learning is used. The “right answers” are not told to systems. What are the things being shown must be figured out by the algorithm! To the desired structure within, we have to first explore the data. On the areas of transactional field unsupervised learning is applied. Considering an example: the customers having similar attributes can be identified with similar segments and all these customers can be given the similar type of marketing campaigns. Or in the other way, the customer segments can be differentiated from each other by finding the main attribute. The techniques like the self-organizing maps, nearest neighbor mapping, k-means clustering and singular value decomposition these all are the important and popular techniques. For segmenting the text topics, recommending items and for identifying data outliers for all these the algorithms are used.
  3. Semi-Supervised learning: as the supervised learning is used similarly for that application only semi-supervised is used. The labeled and unlabeled data both are used for the training similarly the way labeled data were taken in small data and unlabeled data in large quantity as unlabeled data is less in price and requires very fewer efforts to acquire it. The methods of classification, regression, and prediction in which this type of learnings are used. A purely and fully labeled training process is not allowed as the cost of labeling gets very high in this type of situations the semi-supervised learning is used. Identification of a person’s face through the webcam is one of the examples.
  4. Reinforcement learning: this basically includes robotics, gaming, and navigations. Which action will get the best reward is being identified by the trial and error method by the algorithm this whole method is called as reinforcement learning. The agent or the decision maker, then come to the environment that place or those everything the agent interacts with, finally the actions or the steps taken by the agent- these are the important and primary three components of reinforcement learning. To increase the expected reward and that too in a stipulated period of time the agent has to choose the appropriate action which the agents main objective. Having a good policy is an important aspect through which the agent can reach his goal much easily and fastly. In reinforcement learning knowing the best policy is the key aspect.

SECTORS WHICH ARE BENEFITED WITH MACHINE LEARNING

  • FINANCIAL SECTOR: the financial sector which included banks, investment companies, and other businesses uses this machine learning techniques for main two key purposes that are: first is the fraud prevention, second is to identify the important insights present in data. From this, the investors can easily know when to trade and can identify the investment opportunities from the insights. Another technology data mining can also be used for identifying clients with high-risk profiles and warning the clients about the fraud by using cyber surveillance which gives pinpoint information.
  • GOVERNMENT SECTORS: machine learning has a lot of importance in public safety and utilities in Government sectors as the data can be mined easily for insights as they have a lot of or multi sources for the data. Analyzing the sensor data for example which increases and identifies the efficiency and saves the money. Detecting fraud and minimizing the theft are the main things for which machine learning is used.
  • MEDICAL SECTORS: in medical sectors, machine learning is having a lot of hype all thanks to the new trending wearable devices and sensors that are used by the doctors to treat their patients. Using this technique the doctors can easily check or diagnose the data to analyze the trends or red flags which improves the treatment.
  • SALES AND MARKETING SECTORS: you can easily analyze your previous buying history when the website recommends you the similar items based on your last buying which automatically helps in promoting other items. It helps a lot to analyze the given data, capture it and use the same to get a customized shopping experience.
    OIL AND GAS SECTORS: machine learning does very important work in the oil and gas sectors like discovering new energy sources, analyzing the minerals present in the ground, predicting the sensor failures in the refinery. Making the oil distribution streamlining more efficient and cost-effective. There are a lot of uses of machine learning in this sector which still are still expanding day by day.
  • TRANSPORTATION SECTOR: the key aspect in the transportation industry is to identify the pattern and trends by analyzing the data, which helps the transportation sector a lot by predicting the dangers coming and making the relevant routes and increase the profit percentage. The analysis of data and the modeling aspects are very important in the delivery companies, public transportation and in different transportation companies.

REFERENCES
http://www.expertsystem.com/machine-learning-definition/
https://www.digitalocean.com/community/tutorials/an-introduction-to-machine-learning
https://www.datascience.com/blog/introduction-to-machine-learning-algorithms
https://www.sas.com/en_in/insights/analytics/machine-learning.html

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