I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots. ...

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I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots.

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Machine Learning: An Overview

By shiwaneeg |Email | Mar 22, 2018 | 8649 Views

Machine learning has become one of the most talked about subjects in the world today. It has made it easier for computers to produce models that can very quickly analyze large swaths of complex data and produce accurate results. For any organization, this means there is a better chance of identifying profitable opportunities while minimizing risks and human error.

The automobile industry, banks, governments, retail brands, and certain manufacturers are already taking advantage of machine learning technology in innovative ways:

  • Self-driving cars with automatic emergency response systems are products of machine learning
  • Governments use pattern recognition in images and videos to identify threats and enhance security
  • Retail outlets use it to customize customer experience
  • Banks use big data to market new products, balance risk, and detect fraud
  • Social media platforms, e-commerce companies, and streaming services use it to improve their product selections and recommendations.

There are so many other ways machine learning can be used to make business smoother and life easier; these are just a tip of the iceberg.

To better understand how machine learning works, you need to know the different types and how machines function under them.

The two types of machine learning are supervised and unsupervised learning.

Supervised machine learning

Under supervised machine learning, your data is labeled so that your algorithm can learn from it. On one hand, you have the input variables and, on the other, you have a corresponding output variable.

For your algorithm to master the relationship between both sets of variables, you have to create a mapping function. Once the algorithm learns this mapping function, it will then be able to make accurate predictions on its own in the future.

Here's an example:

You feed a machine with data about your customers. You teach it to identify high-value customers by the amount of money they spend on your business per month. You label the data with the customer's name, amount spent per month, and items bought; these are the input variables.

Then you attached a value (1, 2, 3, n) to the customer's name to indicate whether they are high-value or not (this is the output variable). If you input enough data, the machine will then be able to identify customer value on its own in the future. This can then be used for promotional purposes targeting special customers. You can use the information to determine which customers will most likely respond positively to your marketing efforts.

The majority of practical machine learning is supervised. Work Fusion, a software solution company, offers a service of this nature called Smart Process Automation.

Unsupervised machine learning

With unsupervised learning, you have input variables but no corresponding output variables. The goal here is for the algorithm to determine the inherent characteristics and features in the data on its own.

In the process, you learn more about the data and even start to notice things you couldn't have noticed on your own. In unsupervised learning, you feed the machine with customer data but you do not label it. The machine will comb through and start to notice inherent features and characteristics in the data on its own.

The machine learns on its own to separate customer data by spending levels, residential addresses or postal code, gender, age, categories most shopped in, etc.

This was originally published in Business2Community.

Source: Business2Community