Machine learning- Myths About Implementing Machine learning in a Business

By | August 23, 2019
Machine Learning

Lack of knowledge about implementing machine learning projects could produce disastrous results. Without understanding ins and outs of what makes artificial intelligence and machine learning successful, a business can’t implement it. Without deep understanding, implementing machine learning could be a daunting process for executives.

Here are some misconceptions about implementing machine learning and artificial intelligence to business.

A huge amount of data is required for machine learning:

One of the biggest myths about implementing machine learning to a business is, a huge amount of data is required for a machine to learn. Executives believe that more data can increase the ability to hone in actionable insights. They are right, in some cases, but the reverse could be true more often.

If data is relevant that adds to the whole picture than it could be beneficial. The program can suffer from overfitting if data doesn’t fit the machine learning model. Due to overfitting machine learning results fail to appear. Your data must be according to the model of machine. For instance, if a business is training a machine for a medical purpose, id card patterns are useless data. Not only this, extraneous data can cause many other serious problems.

Executives and other stakeholders need to brainstorm and figure out before setting up machine learning that, which data will provide the right basis for moving forward.

According to Jason Brownlee, “the cause of poor performance in machine learning is either underfitting or overfitting of the data.”

Existing data is good enough:

Machine learning processes work on very precise data models, don’t use any irrelevant data. Unless data is clearly targeted and culled or evaluated to account for things like variance and bias, the data isn’t good enough.

Uncontrolled bias is a very common term that you will hear a lot in the machine learning world. If a company have any data that could be used in machine learning, instead of using it as it is, make it precise by removing irrelevant data.

Our business is in the initial stages:

To wade into machine learning, some companies believe that they should spend some time without machine learning, this is completely wrong though. If you are a business, you should transform your business digitally, as soon as possible, to increase gains and prevent cyber thefts. Actually, emerging is exactly the time to transform a business digitally and install artificial intelligence and machine learning, according to a lot of entrepreneurs and innovators.

Stay ahead of the curve of IT trend to stay successful. In the initial stages of a business artificial intelligence can save you from losses due to cyber frauds. For instance, electronic KYC is crucial to know your customer online and prevent financial scams.

Machine learning models are always the same:

Machine learning models are not always the same, there is a wide spectrum of machine learning programs. Some of them essentially run off of a single algorithm while others are based on multiple algorithms. These machine learning algorithms are mathematically transparent and legible.

Many machine learning processes are harder to understand due to their complexity. For example, neural networks, engineers have a hard time to understand and explain how the algorithms work. Most often it takes time to track data through the system.

Depending upon the type of business, machine learning models could be transparent or opaque.

Machine learning only works with curated data:

There are two types of machine learning, both types of learnings work on a fundamentally different basis depending upon the requirement.

Supervised machine learning is one type of machine learning that deals with the labeled data. It means training data can describe categories and properties because it already has labels.

The second type of machine learning is called unsupervised machine learning. Unsupervised machine learning deals with unlabeled data.

Unsupervised machine learning essentially analyzes raw data provided to it and distinguish its characteristics to group it into categories for later use. For both types of machine learning, there’s a lot of potentials, but for supervised machine learning with labeled data, it’s easier to set up a program.


These are some of the misconceptions about digitalizing a business using artificial intelligence and machine learning. These are also some considerations you must have before implementing machine learning. These misconceptions can cause problems for you and for your enterprise in the adoption of machine learning. Removing these misconceptions and installing machine learning in an appropriate way can increase profits by reducing cyber thefts. Here are some key takeaways, relevant data is required for machine learning and use existing data but don’t depend on it.