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MACHINE LEARNING FOR EVERYONE:
FROM HYPE TO HUMANIZATION

2. Danny and Mike are two friends, attending the course
on Machine Learning organized by their school. Just
after the first class, they both indulge in a long
conversation.
Mike: Hey Dan, what a fantastic session it was! I didn’t
know that machines have such great potential with
learning.
Danny: I didn’t get you, Mike? Learning is an explicit
quality, and humans have indeed taught the machines
this art of learning on their own, which we commonly
known as machine learning.
Mike: So, you mean that we humans do not need to
imply our knowledge of our day-to-day tasks, because
machines can do it for us, right? I thought that the
actual machine learning is just a hype created in
society!
Danny: Ohh, it seems that you still don’t know where
and how machine learning has established its
footprints in varied applications industry-wide.

3. Mike: Uhm, yeah, like I used to think that scientists
feed programs into machines and then machines can
work for humans. That’s it.
Danny: Mikey, my friend, I think you haven’t yet come
across real-time use-cases of machine learning. I must
tell you so much.
Mike: Certainly, please do educate me about the
scope and reach of machine learning.
Danny: Mike, let me brief you about what is machine
learning? First of all, Machine learning is a subfield of
Artificial Intelligence, where ML developers feed data
in computer systems and program them to use that
data to learn on their own without human intervention.
To explain it better, I am going to educate you about
the varied applications of machine learning across
fields such as- healthcare, banking & finance, data
analytics, marketing & sales, eCommerce,
cybersvecurity, commuting, and email communications.

4. Machine Learning
Applications
Across Different
Industries

5. Machine learning can monitor mother and fetus health, and enable
diagnosis of any complications. This life-saving power of ML lowers the
risks of miscarriages and pregnancy-related diseases.
Machine learning incorporates several tools and techniques used for
prognostic and diagnostic issues of everyday lives in the healthcare domain.
Some of the common use-cases of machine learning in the healthcare sector
are,
ML for Detection and Diagnosis of Disease-
Machine vision is at an early adoption for detecting symptoms of the patients
so that doctors can address patients’ health beforehand. Also, doctors are
attempting medicine recommendations, overall diagnosis, and readmissions
for social advantage.
Treatment Recommendation-
Depending on the patient’s previous health record, machine learning can aptly
recommend which treatment is going to suit-well to the patient.
Discovery of Drugs-
Healthcare and drug research are a crucial industry impacting the overall
development of a nation. Machine learning also helps in providing approaches
that improve decision-making while drug-selection with abundant specified-
questions over a vast data pool.
Machine Learning Approach for Prediction of Pregnancy-
Machine Learning in Healthcare

6. Most of the banking and finance matters deal with vast amounts of data, and
machine learning works well with large volumes of data to detect anomalies and
nuances. Some of the implications of machine learning in the banking and
finance industry are,
Fraud Detection-
Programmers train machines to catch abnormal finance behaviors, and this way,
finance companies, and banks are saving on their operational costs by limiting
exposure to fraudulent activities.
Credit Scoring-
Banks use AI intended credit lending applications to find out low-performing
loans and credit scores of their customers.
Insurance Underwriting-
AI analyzes a variety of factors in the credit decision-making process like the
behavior of traditionally underserved borrowers and avails acute underwriting.
Money laundering prevention-
machines detect suspicious activities, and hence finding fraud is easy to prevent
money laundering.
Robo Advisory-
Customers use AI chatbot applications and mobile assistance to reach their
financial targets. These AI/ML apps assist them with their spending rates,
managing savings, and more.
Machine Learning in Banking & Finance

7. Machine Learning to Boost your Predictive Analytics
Machines Learning works wonders for handling large data sets by
giving predictive results in a short time. It reduces the work of
computer-coders as machines learn on their own by getting real-time
data. Some use-cases of machine learning in data analytics,
Analytics platform-
Employers provide quick analysis tools to their employees so that they
can work efficiently with unified data and tools.
Analytics services-
The E2E solution providers satisfy companies’ custom analytics needs
and turnkey solutions.
Real-time analytics-
ML is used to explore data, though unstructured so that users can get
time-sensitive decisions without any disruptions.
Image recognition and visual analytics-
AI algorithms analyze data piles of images and videos and derive
meaningful insights.

8. Machine Learning For Marketing & Sales
Marketing & Sales is all about getting close to the end-customer, and
this can pertain when companies very well know about their customers’
preferences. Machine learning is the ideal choice when assisting
companies in their sales and marketing goals. Let us see a few ML
applications,
Marketing Analytics-
Artificial Intelligence systems analyze, learn, and measure the
marketing efforts of the PR team. These systems give out expert
insights that drive engagement, traffic, and revenue.
Personalized Marketing-
Internet users get ads of visited online products even on other
webpages, which is one of the techniques used by eCommerce
companies for customer-specific marketing.

9. Context aware marketing-
Marketing executives use Machine Vision and Natural Language
Processing for creating context-aware ads for specific users based on
their interests.
Sales forecasting for pre-sales-
Based on previous sales outcomes and customer contacts, brands use
AI automated forecasts for attaining sales accuracy.
Sales content personalization-
AI analyses the needs of high-priority leads by watching their browsing
patterns and matches the right content to meet their requirements.

10. Machine Learning in E-Commerce
The most vital and trending industry today is the eCommerce industry.
Machine Learning is empowering many online businesses in many
ways like,
Recommender systems-
One of the most potent marketing styles currently is on-site
recommendations. ML reveals customized user recommendations that
benefit eCommerce companies, and they have noticed a 30% hike in
sales because of such ML advice.
Content Personalization-
The AI bots study user patterns, interests, and browsing methods and
decide the type of content to display for each user. Such personal-touch
boosts sales amongst the customers.
Chatbots-
AI has taken personalization to a very high-level. Customers feel
personal preference and respond much better with the businesses,
which ultimately benefits the online companies.
Dynamic Pricing-
Studying customers’ browsing history, shopping patterns, times, and
more, ML automates customized sales and discounts for users. Such an
active style of pricing works out favorably for e-businesses.

11. Machine Learning for Cybersecurity
Artificial Intelligence is used to provide security and authentication in
sophisticated systems. Some of them are,
Password protection & Authentication-
Machine learning adapts user profiles by checking their keystroke
styles, typing speeds, and else to provide multi-factor authentication. It
suggests stronger passwords whenever necessary.
Cyber threat detection-
AI detects anomalies early and help the brands discover vulnerabilities
beforehand. AI finds out even the minutest changes in human
interaction patterns to identify red-flags in the process.
Phishing Detection and prevention control-
AI combats the phishing attacks very well by analyzing internal
communication systems and detecting any unusual event.
Vulnerability management-
Machine learning can track hackers’ activities and analyze their attack
patterns. Then, companies can use these ML reports to enlighten their
employees about such attacks so that they stay protected.

12. Predicting Travel Mode of Individuals by ML
AI and ML have been the most useful in reducing the tiring commute
time of workers. Some of the ways how AI is helping to tackle the
complexity of transportation,
Predictions-
Google maps help in sharing users’ locations, which, in turn, helps to
detect the traffic and speed of its movement. This way, ML can
suggest the commuters to find the fastest routes to get to their
destinations.
Ridesharing apps-
ML helps the ride-sharing apps to find out the price of user rides,
waiting times, and detour rides availability. Apps like Lfyt and Uber
are a boon today.
Auto-pilot-
the earliest use of AI is in airplanes. The cockpit requires minimal
human pilot effort. Rest, since 1914, planes have been using AI auto-
pilot mode for flying.

13. Machine Learning to Improve the Email Experience
Email is an essential part of one’s life, and you cannot guess how
magically AI and ML are making your work routine easy. Let’s see,
Spam filters-
AI uses simple filters to keep unwanted emails, marketing emails, and
promotional ones from unknown ids away from your inbox, i.e., in your
spam mails.
Smart Email Categorization-
Gmail uses an efficient AI technique to keep your emails categorized
into primary, promotional, and social mails in groups. Gmail is
continually learning as every time you mark an email important, it
remembers.

14. Danny (continuing): So, Mike, I hope these real-
time examples of Machine learning have brought
insights to you.
Mike: I am excited, Danny! These examples of
machine learning and implementations of
Artificial Intelligence have sparked me up. Now
I know that all the hype I heard about ML is
nothing but a proven reality. Thank you so
much, my friend, Thank you!

15. Wrapping Up
Many of you might have
cleared the perceptions of new
and upcoming ML and AI after
reading this post. Now that you
know the true potential of
machine learning; get the best
AI and ML development
services from Bacancy to yield
high-performing AI/ML apps.

16. Thank
You