Vivid dimensions of machine learning




As the famous saying goes “Humans can typically create one or two good models a week; machine learning can create thousands of models a week”. By Thomas H. Davenport, Analytics thought leader excerpt from The Wall Street Journal.

Machine learning is a manner of data analysis that automates analytical model building. It is a branch of Artificial Intelligence based on the idea that systems can learn from data, identify patterns and make decisions with least human intervention.

Major points to be discussed in this blog are:

1.       About its importance

2.       Who uses it?

3.       How it works

 

Let’s start with the Evolution of Machine Learning

As of new computing technologies, machine learning today is not like the machine learning of the past. It was born from the pattern recognition and the hypothesis that computers can learn without being programmed to perform particular tasks; researchers who were interested in Artificial Intelligence wanted to see if computers could learn from data. While several machine learning algorithms have been around for a long period of time, the capability to automatically implement complex mathematical calculations to big data –faster and faster and over and over – is recent progress.

Here are a few extensively publicized instances of machine learning applications you may be familiar with:

∙         Online recommendation offers such as those from Netflix and Amazon; Machine learning applications for daily life.

∙         The heavily hyped, self-driving Google car; the essence of machine learning

∙         Fraud detection; one of the most obvious, important uses in our world today

∙         Knowing what clients are saying about you on Twitter? Machine learning pooled with linguistic rule creation.

Why is machine learning so important these days?

Resurging interest in machine learning is due to the same aspects that have made Bayesian analysis and data mining more popular than ever. Things like increasing volumes as well as varieties of available data, computational processing that is economical and more powerful, with reasonable data storage.

 

All of these things mean that it is possible to automatically and quick produce models that can analyze more complex data and deliver faster, along with more accurate results, even on a very large scale.

What is needed to create good machine learning systems?

∙         Algorithms – basic and advanced

∙         Data preparation capabilities

∙         Scalability

∙         Automation and iterative processes

∙         Ensemble modeling

 

Machine learning in today's world

By using algorithms to build models that uncover connections, organizations can make superior decisions without the support of any kind of human intervention.

Who is utilizing it?

Most industries working with ample amount of data have recognized the worth of machine learning technology. As per the reports, now several organizations are able to work more capable or gain an advantage over competitors.

Machine learning in the Health care industry:

Machine learning is one of the fast-growing trends in the health care industry, thanks to the beginning of wearable devices and sensors that can utilize data to assess a patient's health in real time. This significant technology can also support medical practitioners to analyze data to recognize trends or red flags that may lead to better diagnoses and treatment.

Machine learning in the Transportation Industry:

Analyzing data to identify trends and patterns are the major key to the transportation sector, which relies on making routes more competent and predicting prospective problems to boost profitability. The data analysis and modeling factors of machine learning are vital tools to delivery companies, public transportation as well as other transportation organizations.

Machine learning in Financial services

Banks and other businesses in the financial sector use machine learning technology for two major purposes: to recognize significant insights in data and to prevent fraud. The insights can spot investment opportunities and scope or help investors know when to do business. Data mining can also recognize clients with high-risk profiles, or utilize cyber surveillance to pin down warning signs of fraud.

 

What are the differences between data mining, machine learning, and deep learning?

Even though all of these methods have a similar objective – to pull out insights, patterns, and relationships that can be utilized to make decisions – they have diverse approaches and abilities.

Data Mining

Data mining can be regarded as a superset of several different methods to pull out insights from data. It might involve conventional statistical methods & machine learning. Data mining applies methods from several different areas to recognize previously unidentified patterns from data. This can contain machine learning, statistical algorithms, time series analysis, text analytics, and other areas of analytics. Data mining also comprises the study and practice of data storage as well as data manipulation

Deep learning

Deep learning combines advances in computing power and special kinds of neural networks to learn complicated patterns in a large quantity of data. Deep learning techniques are currently state of the art for identifying objects in images as well as words in sounds. Researchers are now looking to implement these successes in pattern detection to more intricate tasks such as medical diagnoses, automatic language translation, and numerous other important business as well as social problems.

 

Machine Learning

The most important difference with machine learning is that it is completely like statistical models, the objective is to understand the structure of the data – fit theoretical distributions to the data that are well implicit. So, with statistical models, there is a theory behind every model that is mathematically proven, but this needs that data meets definite strong assumptions too. Machine learning has developed n the basis of the ability to use computers to investigate the data for structure, even if we do not have any theory of what that structure looks like. The test for a machine learning model is a validation fault on new data, not a theoretical test that proves a null hypothesis. As machine learning often utilizes an iterative approach to learn from data, the learning can be very easily automated. Passes are run through the data until a perfect pattern is found.



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