11 Insane Machine Learning Myths Debunked for You!

The world is becoming smart, smarter than ever before. There are homes that know how to turn on the lights by judging their intensity and there are cars that can drive themselves. Isn’t it something like living in a sci-fi world? Everything that was imagined is turning into reality.

With more gadgets and technologies being launched every day, customers are keen to know what is it that is making them smarter? They are curious to discern the tech running behind the smartness and understand how it can benefit them in their personal as well as business ventures.

This inquisitiveness towards the “working” has lured people to read and question about the same, however, the responses have not been palatable. For instance, you may often see mobile companies using the terms artificial intelligence and machine learning interchangeably for their products, now this is how a misperception is shaped. The customers do not understand the difference between the two and start treating them as synonymous with each other.

It is important because machine learning forms an integral part of almost all data-driven work. In the event that you intend to consolidate it into your business, you should discern what it may or may not be able to do for you. Having a clear perspective will ensure that you develop a strategy that fits into your business module and helps you accomplish the set objectives.

You know how they say in school that if your basics are clear, you will understand each and every concept, and if not then surely there will be trouble. This concept will hold true in your entire life and therefore if you recognize the simple notion of machine learning you’ll never be influenced by the related hysterias. The figure below describes machine learning in its most naive form.

There is a lot of reality and there is a lot of hype pertaining to machine learning. But with the above-illustrated diagram, it should be clear that machine learning is, training a machine by giving it a large amount of data and then letting it perform based on that learning.

Machine learning is currently going through a phase of inflated expectations. There are a lot of organizations looking forward to conceptualizing and running ML projects without even exploring the power of basic analytics. How do you expect them to meet their goals when they do not know what ML can or cannot do? In such a scenario it becomes imperative to know the myths and truths related to the subject.

Now in order to do that you provide it with a huge amount of data that contains pictures of all the types of dogs present in the world. With the help of these images, the camera is able to create a pattern that resembles a dog. Now whenever you point the camera towards the dog, it matches the pattern and that is how you get a positive hit. On the other hand, pointing the camera towards a cat doesn’t identify it as anything.

This is a machine learning process where the machine is being trained to accomplish a particular task. Artificial Intelligence on the other hand is a broader concept, where the machines are trained in such a way that they can make their own decisions just like the human brain.

Business firms are spending a lot of money in gathering the best machine learning talent which can analyze their data and offer useful insights. What they forget in the process is that machine learning is just one part of an effective strategy, the basics are to have the right type and amount of data.

If there is no one who can fetch the data, what will the professionals work upon? Therefore, businesses do not need a staff good in one field but someone who knows how to work from the scratch. There are data science firms all over the globe that can help businesses develop a correct approach and provide the useful insights they have been looking for.

Artificial intelligence and machine learning are used in countless ways, and not all of them need to be built from scratch. A simple way to explain this is-

Consider your smartphone. You haven’t made it, but you know how to use it. You use it for professional and personal work, right? ML models are the same. Experts build the models, and you use them in your business. They will help customize the software to meet the enterprise’s requirements.

Isn’t that easier to just let the professionals handle all the work? This not only allows a business owner to explore the problem that needs to be solved but also saves time that he/she would have invested in conducting the ML operations on their grounds.

Why do you think the market is full of AI and ML offshore companies? They do the backend work so that you can directly implement the software in your business systems. At the most, you’ll have to train your employees, and ML consulting companies help with that too.

Machine learning is considered out of bounds by many SMEs and startups. They might think it’s majorly meant only for large enterprises. The truth, however, is far from this assumption. We don’t deny the costs involved. But at the same time, it is not necessary to make a huge investment in machine learning.

Machine learning can be even used for something as simple as automating emails, reports, updating address books, sending reminders, and scheduling phone calls. ML doesn’t have to do the heavy lifting all the time. It can take care of the recurring tasks and save time, money, and effort for small enterprises. The simple reason is that instead of hiring additional employees for entry-level work, you can automate the process and ask an existing employee to oversee it.

Around 50–60% of a data scientist’s time is spent on data collection, data cleaning, and data preparation to feed it to the ML model.

For example, if you want to know why your customers are moving on to other brands, you’ll need to use data sets from the CRM systems. The purchase records, the pricing, customer service, competitors, and even the market conditions can influence a customer’s decision. Data scientists will get the data ready and feed it into the ML model to understand the reason for customers’ disloyalty towards the brand.

Contrary to the popular opinion in the market, deep learning is not a solution to machine learning problems, nor does it work the same way as ML models do.

When we hear so many terms and definitions that sound similar, it’s easier to assume they mean the same thing. Machine Learning and data mining are being considered the same by many people.

Though they are not entirely different and commonly deal with huge data sets, data mining is different from machine learning. The major difference is that ML is a technology and data mining is a technique. The approaches to processing data are also different.

The main reason to use machine learning is for automation and minimizing human intervention, isn’t it? Then how is ML dependent on humans? Here, we are talking about developing machine learning algorithms and models.

Without a programmer writing code, executing, and debugging it, how can the ML algorithm be deployed? Who will feed data into the system for the algorithm to learn? That’s where humans are necessary, and the demand for ML engineers has increased.

A most important factor to remember is that a machine learning model is only as good as the data fed into it. If you enter wrong or poor-quality data, the results will be the same.

The machine learning algorithm is pretty much a catchphrase that led to many people assuming that ML is only about algorithms. Yes, algorithms play a vital role, but some elements are even more crucial for the ML models to function.

These are the three aspects that form the ML framework. Training data is the collection of data sets we first feed the ML algorithm. It learns patterns from this data and applies them to other datasets for analysis. As we mentioned earlier, feeding wrong data at this stage will lead to substandard and defective algorithms.

A black swan is an unforeseen event that can majorly affect a business. While some people believe that ML is 100% accurate, others think ML cannot predict everything, especially black swans. How can a machine predict the most unexpected incidents or events?

Well, it is possible. Machine learning models can actually predict the unpredictable with great accuracy. They are capable of uncovering patterns and trends which humans and ordinary calculations cannot. However, it also depends on the team and the management, who might ignore the prediction, thinking it’s a system lapse or a missed assessment.

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