Machine Learning…..Demystified

Machine Learning…..Demystified

By Daniel Settanni, Senior Cloud Development Architect, TruQua Enterprises

Artificial Intelligence (AI), Machine Learning (ML), Predictive Analytics, Blockchain – with so many different buzzwords, it can be a challenge to understand how they are applicable to your business. Here’s a short primer to help customers make sense of Machine Learning in the Enterprise.

By Daniel Settanni, Senior Cloud Development Architect, TruQua Enterprises

Artificial Intelligence (AI), Machine Learning (ML), Predictive Analytics, Blockchain – with so many different buzzwords, it can be a challenge to understand how they are applicable to your business. Here’s a short primer to help customers make sense of Machine Learning in the Enterprise.

What is Machine Learning?
There are two definitions that seem to be the most commonly referenced when discussing the meaning of Machine Learning. Arthur Samuel coined the phrase Machine Learning in 1959 as a “Field of study that gives computers the ability to learn without being explicitly programmed.”

In 1998, Tom Mitchell added some clarity by stating: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”  Or in short – Machine Learning means that a computer’s performance improves with experience.

While both definitions are accurate, neither provides absolute clarity, so let’s explore the concept of Machine Learning through an example.

An Example of Machine Learning
A large credit organization is struggling to detect fraudulent transactions. They have years’ worth of historical transactions, including those that have been identified as fraudulent. Their goal is to detect suspicious transactions in real time. Putting this example into Tom Mitchell’s formula can be displayed as the following:

E = the experience of analyzing historical transactions
T = the task of reviewing transactions
P = the performance of the program in identifying fraudulent transactions

This is an example of supervised learning, which is another way of saying that the algorithm will be taught by the historical data. This is possible because the data is labeled (i.e., historical fraudulent transactions are known).

When the data isn’t labeled, unsupervised learning is required. An example of unsupervised learning would be market segment analysis. Here, Machine Learning is learning from the data and making its own connections and insights, as opposed to being taught from past outcomes.

The response, or output of Machine Learning can be described as regression or classification.

With regression, the response is a set of continuous values – think of a curve that predicts home prices based on size. A home price could be found for a home of any size.

Classification identifies group membership. For example, Machine Learning could classify images based on their content (i.e., this image contains a car, this one contains a rooster, etc.). Our fraudulent transaction example above is an example of classification.  The Machine Learning algorithm is classifying transactions as either fraudulent, or non-fraudulent.

Machine Learning Implementation Process
Now that you have a high-level overview of what Machine Learning can do, you might be wondering what it takes to implement. The basic process includes the following steps:

1. Understanding the problem that needs to be solved
2. Analyzing and preparing the data
3. Identifying potential algorithm(s)
4. Training,testing and tweaking several Machine Learning models
5. Integrating Machine Learning with existing systems and processes

Another key question you’ll need to ask is, who can do all of this? In some cases, a software vendor can deliver Machine Learning capabilities out-of-the-box. This works best when a problem is well defined and common within a specific business or industry process.

For example, SAP’s Cash Management Application is a perfect example of a solution that can harness the full benefits of machine learning.

But what if an out-of-the-box solution doesn’t exist? This is where you’ll need to go a step further and employ the skills of a data scientist and an area where TruQua can help.

Conclusion
A key thing to keep in mind with Machine Learning is that similar to most projects having to do with data analysis, if your data is inaccurate or full of discrepancies, you won’t achieve a positive end result. As with any project, it’s critical to pick your the right partner and solution provider.

How TruQua can help
TruQua’s team of consultants and data scientists merge theory and practice to help customers gain deeper insights and better visibility into their data for more informed decision-making, utilizing the latest predictive analytic and Machine Learning capabilities from SAP. Contact us today and learn how TruQua can help:

  • Improve business processes
  • Enhance decision making
  • Direct, optimize, and automate decisions to meet defined business goals

Find out how TruQua can take you farther, faster, together.

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