Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
machine learning
1. MACHINE LEARNING
Machine learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed. Machine learning
focuseson the developmentof computer programs that can access data
and use it learn for themselves.
2. INTRODUCTION
The term Machine Learning was coined by Arthur Samuel in 1959, an
American pioneer in the field of computer gaming and artificial
intelligence and stated that “it gives computers the ability to learn
without being explicitly programmed”.
And in 1997, Tom Mitchell gave a “well-posed” mathematical and
relational definition that “A computer program is said to learn from
experience E with respect to some task T and some performance
measure P, if its performance on T, as measured by P, improves with
experience E.
E = the experience of playing many games of checkers
T = the task of playing checkers.
P = the probability that the program will win the next game
3. RE-REQUISITES
TO LEARN
ML:
• Linear Algebra
• Statistics and Probability
• Calculus
• Graph theory
• Programming Skills – Language
such as Python, R, MATLAB, C++ or
Octave
5. FINANCIAL SERVICES
Companies in the financial sector are able to identify key insights in financial
data as well as prevent any occurrences of financial fraud, with the help of
machine learning technology. The technology is also used to identify
opportunities for investments and trade. Usage of cyber surveillance helps in
identifying those individuals or institutions which are prone to financial risk, and
take necessary actions in time to prevent fraud
6. MARKETING AND SALES
Companies are using machine learning technology to analyze the
purchase history of their customers and make personalized product
recommendations for their next purchase. This ability to capture, analyze,
and use customer data to provide a personalized shopping experience is
the future of sales and marketing.
7. GOVERNMENT
Government agencies like utilities and public safety have a specific need
FOR Ml, as they have multiple data sources, which can be mined for
identifying useful patterns and insights. For example sensor data can be
analyzed to identify ways to minimize costs and increase efficiency.
Furthermore, ML can also be used to minimize identity thefts and detect
fraud.
8. HEALTHCARE
With the advent of wearable sensors and devices that use data to access health
of a patient in real time, ML is becoming a fast-growing trend in healthcare.
Sensors in wearable provide real-time patient information, such as overall health
condition, heartbeat, blood pressure and other vital parameters. Doctors and
medical experts can use this information to analyze the health condition of an
individual, draw a pattern from the patient history, and predict the occurrence of
any ailments in the future. The technology also empowers medical experts to
analyze data to identify trends that facilitate better diagnoses and treatment.
9. TRANSPORTATION
Based on the travel history and pattern of traveling across various routes,
machine learning can help transportation companies predict potential
problems that could arise on certain routes, and accordingly advise their
customers to opt for a different route. Transportation firms and delivery
organizations are increasingly using machine learning technology to carry
out data analysis and data modeling to make informed decisions and
help their customers make smart decisions when they travel.
10. OIL AND GAS
This is perhaps the industry that needs the application of machine
learning the most. Right from analyzing underground minerals and
finding new energy sources to streaming oil distribution, ML applications
for this industry are vast and are still expanding.
12. SUPERVISED LEARNING
When an algorithm learns from example
data and associated target responses that
can consistof numeric values or string
labels, such as classes or tags, in order to
later predict the correct response when
posed with new examples comes under the
category of Supervised learning. This
approach is indeed similar to human
learning under the supervision of a teacher.
The teacher provides good examples for the
studentto memorize, and the studentthen
derives general rules from these specific
examples.
13. UNSUPERVISED LEARNING
Whereas when an algorithm learns from plain
examples without any associated response, leaving
to the algorithm to determine the data patterns on
its own. This type of algorithm tends to restructure
the data into something else, such as new features
that may representa class or a new series of un-
correlated values. They are quite useful in providing
humans with insights into the meaning of data and
new useful inputs to supervised machine learning
algorithms.
As a kind of learning, it resembles the methods
humans use to figure out that certain objects or
events are from the same class, such as by
observing the degree of similarity between objects.
Some recommendation systems that you find on
the web in the form of marketing automation are
based on this type of learning.
14. REINFORCEMENT LEARNING
Reinforcementlearning is an area of Machine
Learning. It is about taking suitable action to
maximize reward in a particular situation. It is
employed by various software and machines
to find the best possible behavior or path it
should take in a specific situation.
Reinforcementlearning differs from the
supervised learning in a way that in
supervised learning the training data has the
answer key with it so the model is trained with
the correct answer itself whereas in
reinforcementlearning, there is no answer but
the reinforcementagent decides what to do to
perform the given task. In the absence of
training dataset, it is bound to learn from its
experience.
