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What is Machine Learning & why it is important?

July 7, 2018
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Machine Learning (ML) is new age computing technology. It was born from pattern recognition and theory of computers. It can learn without being programmed to perform specific tasks. Researchers interested in artificial intelligence want to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum. Machine learning is domain of engineering where algorithms and computer based programs are learnt from data and past experience and provides decisions and analysis based on their experience and learned knowledge.



Machine Learning: Basic description

Machine learning is a set of tools that, broadly speaking, allow us to “teach” computers how to perform tasks by providing examples of how they should be done. For example, suppose we wish to write a program to distinguish between valid email messages and unwanted spam. Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. Machine learning is programming computers to optimize a performance criterion using example data or past experience. We need learning in cases where we cannot directly write a computer program to solve a given problem, but need example data or experience.

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that’s gaining fresh momentum. Figure 1 depicts the different interdisciplinary filed of the machine learning.

field of machine learning

Figure 1: Machine Learning Interdisciplinary field

ML introduces stepwise process to learn and utilize the knowledge to solve the real world problems in an effective and efficient manner. This process includes data pre-processing, training on an algorithm and implementation using any application, sometimes the last step is also known as testing of the data model.

Machine learning

Figure 1: Machine Learning Practice

  • Data pre-processing: Data in the real world are never found in a structured manner, which is hidden between unstructured data or between uneven data sources. In this phase data is separated from its original format and organized in a desired format to read and utilized for future use. This technique includes the filtering, manipulation, and transformation of data to make it more suitable and adoptable for use.
  • Algorithm Training: Using the previous phase outcomes, the data model or ML algorithms prepares a data structure or data model by which decisions are made for providing solutions.
  • Testing: Performance of the system can be measured using this phase of the system, here manually or automatically real time or sample data is produced to test the intelligence of trained algorithm.




Machine Learning: Importance

Interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage. All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.

Applications of Machine Learning

Machine learning has been extensively applied in various application domains:

  • Financial services

Banks and other businesses in the financial industry use ML technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cyber surveillance to pinpoint warning signs of fraud.

  • Health care

ML is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment.

  • Marketing and sales

Websites recommending items you might like based on previous purchases are using ML to analyze your buying history – and promote other items you’d be interested in. This ability to capture data, analyze it and use it to personalize a shopping experience (or implement a marketing campaign) is the future of retail

  • Transportation

Analyzing data to identify patterns and trends is to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of ML are important tools to delivery companies, public transportation and other transportation organizations



References

[1] Alex Smola and S. V. N. Vishwanathan, “Introduction to Machine Learning”, Yahoo! Labs, Ph.D. thesis, Cambridge University Press.

[2] Aaron Hertzmann and David Fleet, “Machine Learning and Data Mining Lecture Notes”, Computer Science Department University of Toronto, Version: February 6, 2012

[3] Cathy Pareto, “Understanding Investor Behavior”, available online at: http://www.investopedia.com /articles/05/032905.asp

[4] “Machine Learning: What it is and why it matters”, available online at: http://www.sas.com/en_us/insights/analytics/machine-learning.html

[5] “Machine Learning Basic Concepts”, available online at: https://courses.edx.org/asset-v1:ColumbiaX+CSMM.101x+1T2017+type@asset+block@AI_edx_ml_5.1intro.pdf

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