Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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What Is Machine Learning?

By Nand Kishor |Email | Aug 8, 2017 | 13731 Views

Amazon uses it. Target uses it. Google uses it. "It" is machine learning, and it's revolutionizing the way companies do business worldwide.

Machine learning is the ability for computer programs to analyze big data, extract information automatically, and learn from it. With 250 million active customers and tens of millions of products, Amazon's machine learning makes accurate product recommendations based on the customer's browsing and purchasing behavior almost instantly. No humans could do that.

Target uses machine learning to predict the offline buying behaviors of shoppers. A memorable case study highlights how Target knew a high school girl was pregnant before her parents did.

Google's driverless cars are using machine learning to make our roads safer, and IBM's Watson is making waves in healthcare with its machine learning and cognitive computing power.

Is your business next? Can you think of any deep data analysis or predictions that your company can produce? What impact would it have on your business's bottom line, or how could it give you a competitive edge?

Why Is Machine Learning Important?
Data is being generated faster than at any other time in history. We are now at a point where data analysis cannot be done manually due to the amount of the data. This has driven the rise of MI -- the ability for computer programs to analyze big data and extract information automatically.

The purpose of machine learning is to produce more positive outcomes with increasingly precise predictions. These outcomes are defined by what matters most to you and your company, such as higher sales and increased efficiency.

Every time you search on Google for a local service, you are feeding in valuable data to Google's machine learning algorithm. This allows for Google to produce increasingly more relevant rankings for local businesses that provide that service.

Big Big Data
It's important to remember that the data itself will not produce anything. It's critical to draw accurate insights from that data. The success of machine learning depends upon producing the right learning algorithm and accurate data sets. This will allow a machine to obtain the most efficient insights possible from the information provided. Like human data analysts, one may catch an error another could potentially miss.

Digital Transformation
Machine learning and digital technologies are disrupting every industry. According to Gartner, "Smart machines will enter mainstream adoption by 2021." Adopting early may provide your organization with a major competitive edge. Personally, I'm extremely excited by the trend and recently spent time at Harvard attending its Competing on Business Analytics and Big Data program along with 60 senior global executives from various industries.

Interested In Bringing The Power Of Machine Learning To Your Company?

Here are my recommendations to get started with the help of the right tools and experts:

  1. Secure all of the past data you have collected (offline and online sales data, accounting, customer information, product inventory, etc.). In case you might think your company doesn't generate enough data to require machine learning, I can assure you that there is more data out there than you think, starting with general industry data. Next, think about how you can gather even more data points from all silos of your organization and elsewhere, like chatter about your brand on social media.
  2. Identify the business insights that you would benefit from most. For example, some companies are using learning algorithms for sales lead scoring.
  3. Create a strategy with clear executables to produce the desired outcomes such as fraud protection, higher sales, increased profit margin and the ability to predict customer behavior. Evaluate and revisit this strategy regularly. 

Source: Forbes
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