https://www.datatobiz.com/blog/machine-learning-myths/ 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. Among all that we hear about the upcoming technology, machine learning (ML) is a common term being associated with almost all of them. The term has been more misinterpreted than understood and there has been a considerable measure of hype buzzing around it. 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. The aim here is to make you understand the similarities and differences between “machine learning” and the terms it is confused with. this write-up shall provide you with a clear insight so that you can differentiate between the hype and the reality. 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.

1. Insane Machine Learning
Myths Debunked for You!
11

2. 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.

3. One of the commonest misconceptions is between artificial intelligence and machine learning.
Both the terms are not only different in words but are two different fields belonging to a bigger
pool of data science. In order to understand the difference consider this example – You wish that
the camera of your phone should recognize a dog.
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.
If you put a cat in front of a camera that works on an AI technology, it will use it as another input
and further reuse it to train itself. This training would help the AI-enabled phone to tell that isn’t a
dog but it may be something else that can be explored.
#1 Machine Learning and Artificial Intelligence
Are Same

4. 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.
#2 Hiring the Best ML Talent Is Sufficient to
Resolve Business Issues

5. Machine learning sounds scientific and complicated that many presume it is not meant for
their business. After all, what will an ordinary business do with advanced technology? Not
every SME hires AI experts, isn’t it? That’s where we are wrong
Years before it was said that if you wish to carry our ML operations on your premises, you’ll
need to invest a large amount in infrastructure. The scenarios have changed now. Since data
science has become such an integral part of the business world, there are professionals who
are teaming up to form organizations that work purely on data and offer all the insights you
want.
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.
#3 ML Implementation Requires Humongous
Infrastructure

6. 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.
In fact, adopting AI and ML during the earlier stages of the business will help you get used to
advanced technology at work and maintain high working standards.
#4 ML Is Only for Large Enterprises

7. Artificial intelligence, machine learning, and data science are interrelated even though they are
not the same. Experts say that machine learning is essential for data scientists to deliver ‘high-
value predictions’ in real-time. However, data science is not limited to building machine learning
models.
Data science is a field where mathematics, statistics, and computer science are combined and
used to derive actionable insights from raw data. The processes and algorithms are complex and
can analyze vast amounts of data in a quick time. However, data science is not the ultimate
solution to every business problem.
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.
#5 Data Science Means to Build Machine
Learning Models

8. Deep learning is actually a subset of machine learning and a highly intricate neural network with
multiple layers. The artificial neural networks (ANNs) try to mimic the human brain to understand
data. In fact, neural networks are considered the backbone of deep learning algorithms. The
deep learning algorithm should have at least three neural networks.
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 you build a deep learning model, you are creating a predictive system capable of
generalizing and adapting to specific conditions of the business. While machine learning extracts
actionable insights by processing data sets, deep learning predicts future scenarios based on
past and real-time data. A highly advanced deep learning model is dynamic and can work in
sync with the changes in the business.
#6 Machine Learning and Deep Learning Are
the Same

9. Read the full article
https://www.datatobiz.com/blog/machine-learning-myths/