An age where humans perfect on machines to be self-taught won’t be far away. Even though
these kinds of technologies are in their infancy, we are seeing some movements that might
eventuate into people creating machines capable of self-improvement. But would it be like what
we see in science fiction movies? This is a matter we need to wait and see.
Imagine having a ‘Baymax’ (the chubby robot in the Disney movie Big Hero 6) in real life. What
about “Terminator”? Yes, but we prefer the good ones, not the villains.
So, does this ‘Machine Learning’ have anything to do with robots?
In a way, yes.
But we are going to explain to you that it is not only about robots but way deeper. Machine
Learning has now become integral in almost all our industries. Businesses, governments, and
organizations are using technologies that work with machine learning to get their job done in the
most effective way possible. This makes Machine Learning relevant, and worth investing your
career or business in.
Dave Waters (founder of Coursera) says – “A baby learns to crawl, walk and then run. We are in
the crawling stage when it comes to applying machine learning.”
Even though Machine learning is in its crawling stage, it tells our future to be more exciting in
terms of innovation. Let us break ‘Machine Learning’ into sizable bits so that we can properly
what machine learning is?
how does it work?
where is it used?
how is it affecting our living world?
can we learn it too?
and all the rest of the questions associated with it.
What is Machine Learning (in simple terms)?
Machine Learning (ML) is a study of designing and developing programs that enable computers
to self-learn, where they could use data to make decisions, predictions, and improvements and
be more autonomous with less human intervention.
‘Machine Learning reduces the need for programming often.’
Machine Learning programs are complex pieces that build on sample data which lessens the
necessity of instructing the system frequently. For any type of work, large or small, there are
tedious cycles and symmetrical patterns which collide, interact, and influence each other to form
the outcome. By identifying the patterns of a particular work humans construct
models(programs) with which computers would be able to analyze and predict better outcomes
for every situation. Well, we could say this is how a normal program function. But what is the
difference when a program is developed through Machine Learning? It’s the ‘learner’ aspect of
the program. It could change the outcomes based on the type of input data. Simply put, by
comparing the statistics and predictions of the input data with given standard values a computer
can adjust the program model to give better outcomes.
“Machine learning is able to utilize data that is both large in size, but also in a different form
than what would traditionally fit into an Excel spreadsheet. We are now able to work on all
these rich new sources of visual data”, says Harvard Business School’s Prithwiraj (Raj)
Choudhury, the Lumry Family Associate Professor in the Technology and Operations
Are we already enjoying the benefits of Machine Learning?
We are seeing it and using it every day. Machine learning runs in popular services that we use
every day. The recommendation systems that we see on Netflix, YouTube, and Spotify are all
Machine Learning. Youtube-like services use algorithms to study and identify our activities and
give us recommendations based on our search. Also, search engines like Google and Bing give
search results by calculating our previous behavior. So are social media networks like Facebook,
Twitter, LinkedIn, and Instagram giving content relating to our search preferences. Voice
assistants like Siri and Alexa have machine learning embedded in them. This said, machine
learning is not applied in our entertainment space alone, it runs everywhere, in every major
industry. We just gave you examples that you could easily identify with. IT, HealthCare, Fintech,
Manufacturing, Retail, Government Organizations, and the rest of the sectors uses Machine
Self-driving cars have machine learning!
How does Machine Learning work?
We have explained Machine Learning – as a discipline that studies, research, and invents working
programming models that train computer systems to be less dependent on frequent human
interventions and less susceptible to being explicitly guided. This is by far the concise definition
of Machine Learning.
Now we shall look into how machine learning systems work to get a better understanding of the
Machine learning systems constitute three major parts.
This is the structural frame. Humans develop models that could make all the possible
identifications and predictions if relevant data is given. Models must be programmed and need
to be integrated into the system. These programs study the patterns and activities based on the
incoming data. Consider an eCommerce business where selling is done through an online
platform. One of the main factors for their business to thrive is to understand customer behavior.
They must analyze the customer behavior to make an informed decision on stocking their
inventory. First, they need to identify all factors that govern customers’ buying preferences and
need to link them with market conditions. When all factors are identified relating to customer
behavior, those factors are used to construct a model (program) for their business. Once they
get relevant customer data from their eCommerce selling platform, these data are fed into the
model to bring predictions that could help them anticipate the market.
Parameters are optimal values of the factors used by the program to make predictions and
decisions. Consider the model (program) as a human skeleton and the joints of the skeleton to
be the parameters. Joints move the skeleton without losing the form factor, it makes every
human move. Like that, parameters in a program give a range for the solutions, from which the
system can choose optimal ones based on the input data.
Parameters are reference points that give freedom to the system to make necessary changes in
calculations to give the best possible result.
We can take the example of an eCommerce business once again. Certain factors might influence
the customer to buy something online when they visit an eCommerce website. It would be the
quality of images of the products, the description of the products, or the competitive pricing of
the products. These are the parameters. By doing statistical analysis of the customer data, the
program with machine learning capabilities assimilates a range of values from the above factors.
Judging from the figures it gives suggestions needed to get better results. Suppose the system
gives a lesser number of the quality-images we could take suitable measures to improve those
As told before, the ‘learner’ aspect makes all the difference. The learner system looks at the
data and makes changes to the model to give better outcomes. Data that is given into the
system is called training data. The learner system stores the prediction results whenever the
program runs and realigns the model by comparing it with predictive outcomes and the actual
How does the learner system do the job?
The learner system is designed with complex mathematical equations to make interpretations
and self-improvements based on the training data. The learner system is used for three main
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning
The Machine Learning system is trained with data samples having known results. Each sample
already has a known result. A machine learning system will learn the relationship between the
parameters and results of each sample of data.
Machine Learning systems will find patterns in the data and the relationship between them
without knowing the individual results of each sample data.
Reward-based learning, where the machine learning system is encouraged for taking the right
actions and penalized for the wrong ones. This way machine learning system influences the
program to rewrite algorithms to choose the right actions and earn high rewards.
Where is Machine Learning used? Does it have any relationship with Artificial
Before getting into where Machine Learning is used, we need to look if Artificial Intelligence has
anything to do with machine learning. Some of us might have the notion both AI and ML are the
same thing. Well, it is not. But they are correlated. Artificial Intelligence is a field in technology
where computer systems are programmed to perform intellectual tasks. Machine Learning is an
approach in AI where machines are given access to data so they can be able to learn by
themselves and improve in giving better solutions to problems.
For a normal computer program, there will be a classic algorithm, where the instructions are
written. It will take the data and give the result that it is supposed to give. Say, converting a
‘document to pdf’. We give the document file to the program, and it will convert it into a pdf.
But when it comes to programs with machine learning, it is trained with examples of data so that
they could find patterns and construct algorithms that could give results according to a different
type of data. ML works on a large amount of data.
Machine Learning has two main organs
We could say neural networks as human brain equivalents (not exactly the brain of course, but
they are designed to imitate how our brain operates). A neural network is a collection of
algorithms that calculates relationships between large volumes of data sets. It functions in a
mode just like how our brain does. Each unit in a neural network is called a Neuron. Each neuron
has certain sets of parameters which it operates on and delivers a result.
Deep Learning deals with a whole set of deep neural networks. DL comprises complex machine
learning methods that enable programs the capability to self-learn. Deep Learning gives the
program the learning power.
‘AI is automating sophisticated intelligent tasks involving decision making, and improvising
methodologies. Machine Learning is one of the branches inside the AI domain.’
Now that we have established the connection between AI and ML, we can discuss where Machine
Learning is applied in our real world.
Application of Machine Learning
We have already mentioned that Machine learning is being used in every sector of our society.
Explaining all the applications of machine learning would make a separate blog post. We shall
take a few examples of machine learning that covers some of the major industries of our world.
Market Pricing Optimization
Pricing is a key component for sellers and buyers all around. All major sellers, eCommerce
giants, and retailers are using machine learning to set competitive pricing to boost their sales,
build their customer base and make inventory decisions. Machine learning makes them capable
of deciding and regulating their product pricing by analyzing market conditions, customer buying
behavior patterns, and stock inventory. Machine learning makes it possible to produce wise
decision points by calculating large volumes of data.
We might have heard of dynamic pricing. Retailers and eCommerce companies make real-time
adjustments in pricing by judging the circumstances. This is done through machine learning,
which is otherwise difficult with a huge amount of market data. If pricing optimization is done
manually, it could take a lot of manpower, time, and capital. Implementing machine learning
techniques gives the flexibility to allocate resources to focus on core business, save our valuable
time, compete efficiently, and anticipate pitfalls.
Machine learning is helping businesses analyze customer buying preferences, predict product
demand, forecast market fluctuations, and make data-driven decisions.
Product recommendation systems give users contents/products that would probably make them
love to engage with. Machine learning algorithms identify our patterns from our search behavior,
connections with products, and our liking to set recommendations on products or content that
we would love to subscribe to or buy. This system is already integrated into our search engines
like Google, our movie streaming services like Netflix, eCommerce platforms like Amazon, social
media sites like Facebook, and even our banking portals. Product recommendation is used in
boosting sales, social media marketing, and customer engagement.
Money Management in Fintech
Banking, trading, and financial services are using machine learning technologies to tackle their
challenges. Banking services are using predictive systems to give better investment strategies
for their customers. Error-free processing and secure transactions are automated with machine
learning. The trading sector is also seeing the large-scale implementation of machine learning
for evaluating risks, price assessments, and digging investment opportunities. Technologies like
Generative Adversarial Networks (GANs) can analyze and solve problems in the financial sector
that were problematic before with raw data. Advisory services use ML to leverage financial big
data to harness profitable outcomes.
Marketing in digital mediums requires deep knowledge of audience targeting, audience
retention, customer preferences, and content optimizations. Machine learning uses data to find
patterns in customer actions and gives metrics and recommendations for making effective
decisions that will accelerate business growth. The identifying target audience will help
marketers launch campaigns that could achieve high conversion rates compared to general
Individualization and personalization of content are core factors in cementing customer
engagement. Marketing analytics by machine learning platforms could suggest data-driven
insights and strategies to run digital marketing campaigns in a way that could result in higher
customer engagement. Social media engagement data is used for influencing target customers.
ML systems can also give data-driven opinions on selecting suitable social media platforms
based on audience nature and the product.
Internet of Things (IoT)
Devices like smart watches, smart TVs, home automation, voice assistants, or any objects that
use the internet to function and exchange data between other connected devices and give us
some assistance could be called the Internet of Things (IoT). Machine learning is predicted to get
more infused with IoT devices than ever and even more as we go, mainly due to the heavy
amounts of data these devices are dealing with, and the amount of learning and personalization
required due to its proximity with people. Smartwatches that could call emergency service by
predicting the possibility of life-threatening conditions like ‘heart attack’ won’t be far away.
These are some of the sectors where machine learning systems are used, but there are many
more. It would be difficult to find any establishments that won’t be using even a tiny level of
automation in their process.
Machine Learning using Python Language
Now, the most relevant question. If you feel machine learning is something that you need to
learn, you would be wondering “how to begin with”.
When we have a new endeavor, it is always better to look at the experts in that field. Python is
one of the best programming languages to develop machine learning systems. Developers adore
the frameworks, the extensive libraries, the community support, the easy learning curve, and
the readable codes, making python top among the choices for machine learning, deep learning,
and artificial intelligence projects.