This document provides an overview of artificial intelligence and machine learning. It discusses how investment in AI is growing rapidly, with the top industries adopting AI being banking, healthcare, and retail. It then defines machine learning and describes common machine learning algorithms like supervised learning, unsupervised learning, and reinforcement learning. The document demonstrates machine learning concepts through examples like fraud detection, recommendation systems, and predicting customer behavior. It also presents a case study of using machine learning to help a Portuguese bank identify clients likely to subscribe to term deposits.

1. MA
Artificial Intelligence &
Machine Learning Overview
By Mukesh Sinha

2. AGENDA
ARTIFICIAL INTELLIGENCE – INDUSTRY OVERVIEW
MACHINE LEARNING – OVERVIEW
TYPES OF MACHINE LEARNING
MACHINE LEARNING – PROBLEM CLASSIFICATION
CASE STUDIES & DEMO

3. ARTIFICIAL INTELLIGENCE – INDUSTRY OVERVIEW
Investment in AI is growing at high rate
In 2016 , Companies invested
$26B to $39B
in artificial intelligence
TECH GIANTS
$20B to $30B
STARTUP
$6B to $9B
3x External Investment growth since 2013
AI Adoptions – Top 3 industries spending ( % of Spend)
Banking
20%
of Spend
Intelligent Financial
health assessment
Credit scoring worthiness
Healthcare
18%
of Spend
Rapid diagnostic with
Medical images
X-Ray, MRI etc
Retail
17%
of Spend
Interactive personalized
experience analyzes
Shoppers History , mood
, expressions etc
Data around the World Top 5 AI Companies
Face Recommendation Navigation Product Recommendation Speech Recognition Medical System
Benefits
Digitially Mature
Larger Business
• JP Morgan has been able to save 360,000 work hours by automating part of their lawyer work by leveraging an
operation efficacy applications
• UBS implemented AI based platform for post-trade allocation requests . Investment banker took about 45 minutes,
to complete the end to end process , AI implementation helped banker to do task in 2 minutes only
• Korean airlines built AI based expert system to redcue the number of flight dealy and cancellations and shortened its
mainteanance lead times by 90%
• NetFlix leaverages AI to tag its movie content and built recommender system for improving customer experience by
offering personalized product recommendations and helping customers to find desired product faster
Increased Automation Productivity
Uncovering new insights
Increased Employee Productivity
• We are pursuing AI to empower every person and every institution ..so that they can go on to solve the most
pressing problems of our society and economy - Satya Nadela , Microsoft
• Building genral artificial intelligence (AI) in a way that helps people meaningfully – I think the word moonshot
is an understatement for that . - Sunder Pichai , Google
• We are living in the golden age of AI , We are now solving problems with machine learning and artificial
intelligence - Jeff Bezos Amazon
• Artificial Intelligence is the new electricity. - Andrew Ng , Baidu
Top Industry Leader Statement on AIAI Use Cases
90 % of World‘s
Data created in past
2 years
3 Billion Online
In 2000, only 738 million
used internet, In 2015
number grew to 3.2 Billion
According to World
Bank, 75% of people
own a cell phone
204 Million
emails are
sent each minute

4. MACHINE LEARNING OVERVIEW
Machine Learning Used
Machine Learning Tools and Services
Trend Forecasting Fraud Detection Price Prediction Product Recommendation
Interesting Case Studies
Large US based Retail Grocery Store
Observation : Men between 30-40 years in age shopping between 5
PM and 7 PM on Fridays, who purchased diapers along with beer in
their shopping cart
Analysis : Data Analysis revealed that new fathers tend to buy more
beer , because they are spending less time at the pub.
Solution: Used Data Mining and Anaytics approach Retailer shop
move the beer isle closer to diaper isle
Benefits :35 % increase in sales of both
Large Global Insurance company - AXA
Use Case : Approximately 7-10% of AXA’s customers cause a car
accident every year, about 1% are so-called large-loss cases that
require payouts over $10,000.
Objective : AXA adjusters to understand which clients are at higher
risk in order to optimize the pricing of its policies.
Solution : AXA’s R&D team analyzed the historical data ( Age of
Driver , Address , Age of car etc) and using Machine learning
algorithm to optimizing price by large-loss traffic accident
Benefits : Team achieved 78% accuracy in its predictions
Significant advantage for optimizing insurance cost and pricing,
Machine Learning - How it works
Learn from Experience Learn from Data Follow Instructions
What is 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.
Human ComputerMachine Learning

5. TYPES OF MACHINE LEARNING
• Make machine learn explicitly
• Data with clearly defined output is given
• Direct feedback is given
• Predicts outcome or future
Supervised Learning
Teacher teaches kids and
providing the solution to them
Unsupervised Learning
• Machine understand the data ( identifies
pattern and data
• Evaluation is qualitative or indirect
• Does not predict or find anything specific
Predicting the weather forecast
Kids are taking decisions out of
their own understanding
Reinforcement Learning
• Reward based learning
• Learning from +ve and –ve reinforcement
• Machine learns how to act in certain environment
• To Maximize rewards
Kids will take actions on his own
from past exp, and parent
providing the feedback
Decision Tree Random Forest Naïve Bayes Support Vector machineK-Means ClusteringTensorFlow
MACHINE LEARNING ALGORITHM
You Tube Recommendation videos System which play chess
Linear Regression Logistic Regression

6. MACHINE LEARNING – PROBLEM CLASSIFICATION
Classification Algorithm
Anomaly Detection Algorithm
Regression Algorithm
Clustering Algorithm
Reinforcement Algorithm
• Used to classify record
• Limited number of answers
• Analyze the pattern
• Alerts in case of change in pattern
• Used to calculate numeric value
• Predicts outcome
• Sepeartes the data into groups or clusters
• Ease out the interpreation of data
• Design how brains of humans respond
• Learn from outcome , reward , punishment
Email Spam for Gmail and Yahoo
Prediction of house price
QUESTIONS ML ALGORITHM ALGORITHM DESCRIPTION EXAMPLE
Fraud Detection for Credit card
Product Recommendation
Google self drive car
How much or how many ?3
Is this weird ?2
Is this A or B?1
How is this organized ?4
What should I do next?5

7. Business Problem
ML DEMO - PORTUGUESE BANK
age job marital education default balance Housing loan contact day month duration campaign pdays previous poutcome Y
30 unemployed married primary no 1787 no no cellular 19 oct 79 1 -1 0 unknown No
33 services married secondary no 4789 yes yes cellular 11 may 220 1 339 4 failure no
30 management married tertiary no 1221 yes no telephone 25 jul 279 4 -1 0 unknown yes
• There has been a revenue decline for the Portuguese bank and they would like to know what actions to take.
• After investigation, they found out that the root cause is that their clients are not depositing as frequently as before.
• Knowing that term deposits allow banks to hold onto a deposit for a specific amount of time, so banks can invest in higher gain financial products to make a profit.
• In addition, banks also hold better chance to persuade term deposit clients into buying other products such as funds or insurance to further increase their revenues.
• As a result, the Portuguese bank would like to identify existing clients that have higher chance to subscribe for a term deposit and focus marketing effort on such clients.
To predict which clients are more likely to subscribe for term deposits
Data Set Description
Name Description and Values
Personal Client Information
Age Age at the contact date (Numeric ≥18)
Marital Status Married, Single , Divorced, Widowed etc
Sex Male or Female
Job Unemployed , 'entrepreneur, technician
Name Description and Values
Bank Client Information
Annual Balance in Euro currency (Numeric )
Default ( Credit) Yes , No , Unknown
Housing Loan Yes , No , Unknown
Pesonal Loan Yes , No , Unknown
Name Description and Values
Last contact information
Contact Type contact communication type
Date When the contact was made
Duration Duration of the contact
Name Description and Values
Other Attributes
campaign No of contacts performed during this campaign
pdays No of contacts performed before this campaign
poutcome Outcome of the previous compaign
Output variable (desired target):
y - has the client subscribed a term deposit? (binary: 'yes','no')
Data Set
Business Goal

8. PYTHON DEMO STEPS FOR BANKING SCENARIO
Import Python Library Packages Pandas , numpy, matplotlib
Loading the Banking data and reading the file
Drop the variables which are not required for prediction
Spliting the data into training set and testing set
Applied logistic regression model using training data set
Checking the score or accuracy of the model
Library File
Data Set
Splitting the Data Set
Data Filter
ML Model
Accuracy
DESCRIPTION PYTHON CODESTEPS

9. REFERENCES
Machine Learning book - Tom Mitchel The Elements of Statistical Learning book -Trevor Hastie
Video Lecture - Andrew NG
Video Lecture - Yaser Abu Mostafa
Artificial Intellignece Reports from IDC , PWC , DB and McKinsey