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Artificial
Intelligence and
Machine Learning
Introduction for
Anyone and
Everyone
www.BigDataTrunk.com
We will be starting soon
Agenda
1. Introductions
2. Machine	Learning	Concepts
3. ML	vs	DL	vs	AI
4. ML	and	AI	Offerings
5. AI	Use	cases
www.bigdatatrunk.com2
Big	Data	Trunk
3 www.bigdatatrunk.com
Introduction	– Big	Data	Trunk
4 www.bigdatatrunk.com
Raju	Shreewastava
• 21	yrs.	in	Data	&	Analytics
• Founder	of	Big	Data	Trunk
• Passion	for	teaching	&	Sharing
• Presented	in	several	conferences
www.bigdatatrunk.com
Cloud	Expo
Santa Clara Convention center
www.bigdatatrunk.com
Author
• Big	Data	High	Availability	(Pearson	
Publication)
• Perform	Data	Engineering	on	Microsoft	Azure	
HDInsight	(Microsoft	Press)
Your	turn
• Working	in	
• Software?
• Data?
• Big	Data?
• ML?
• AI?
7 www.bigdatatrunk.com
www.bigdatatrunk.com
When did Artificial Intelligence
start?
www.bigdatatrunk.com
1950 – Turing test
www.bigdatatrunk.com
History of AI
• 1950 – Turing Test
• 1951 – First Neural Network
• 1967 – “Nearest Neighbor”
Algorithm is written
• 1974 – First AI winter
• 1979 – Stanford Cart
• 1996 – IBM Deep Blue
beats Garry Kasparov
Source https://www.bbc.com/timelines/zypd97h
• 2006 - Geoffrey Hinton coins the
term “deep learning”
• 2014 – Facebook develops
DeepFace & Google Buys
DeepMind
• 2015 – Amazon, Google & Microsoft
ML offerings
• 2016 – AlphaGo beats Lee Sedol
• 2030 – Singularity?
www.bigdatatrunk.com
2045 – Singularity?
AI	is	already	happening
12 www.bigdatatrunk.com
Artificial	Intelligence	v/s	
Machine	Learning	v/s	
Deep	Learning
Concepts	and	Terminology
13 www.bigdatatrunk.com
What	is	Machine	Learning?
Concepts	and	Terminology
14 www.bigdatatrunk.com
Programming	v/s	Machine	Learning
15 www.bigdatatrunk.com
X=	1	,	2	,	3	,	4
1	,	4	,	9	,	16
Square	(X)
X=	1	,	2	,	3	,	4
1	,	8	,	27,	64
Y=Cube	(X)
5
125
Computer
Input	Data
Program
Output
Output
Computer
Input	Data
www.bigdatatrunk.com
Supervised vs Unsupervised
Learning
Concepts and Terminology
www.bigdatatrunk.com
Supervised Learning
(Train Data - labeled )
Unsupervised Learning
(No Train Data – No labels )
KIDS ALIENS
Supervised	Learning
18 www.bigdatatrunk.com
Diameter, Thickness à
Features
Currency à Label
Diameter Thickness Currency
1.0430	inches 0.0790	inches US	dollar	coin
1.0433	inches 0.07680	inches Canadian	dollar	coin	
(Loonie)
0.9154	inches 0.09173	inches One	Euro	coin
• Supervised	learning	
uses	labeled	data	to	
train	the	model
• Forecast	an	outcome
Unsupervised	Learning
19 www.bigdatatrunk.com
Hitters
Pitchers
Hits
Number	of		Innings
• Unsupervised	learning	is	where	there	is	no	labeled	data,	model	
creates	clusters/groupings
• Discover	underlying	patterns	and	capture	useful	insights
• Used	in	recommendation	systems,	anomaly	detection
www.bigdatatrunk.com
Main Machine Learning
Techniques
20
Classification Clustering Regression
Classification	– Supervised	Learning
21 www.bigdatatrunk.com
Weight	=	=	heavy?
High	Mileage Horsepower	<	80
High	MileageLow	Mileage
yes
yes
no
no
• Classification	is	about	predicting	a	label	or	a	class
• Classify	emails	as	Spam	or	Not	Spam
www.bigdatatrunk.com22
Clustering	– Unsupervised	Learning
www.bigdatatrunk.com
Child’s
Weight
Prediction
23
Regression	– Supervised	Learning
www.bigdatatrunk.com
Unhappy Customer
vs
Happy Customer
Customer Engagement
Predictive Analytics
www.bigdatatrunk.comSource Microsoft
www.bigdatatrunk.com
Terminology & Concepts
Algorithms
35 www.bigdatatrunk.com
Popular	Algorithms	in	Machine	Learning
36 www.bigdatatrunk.com
Supervised
Linear	Regression
Random	Forest
Logistic	Regression
Super	Vector	Machine	
(SVM)
K	Nearest	Neighbors	
(KNN)
Decision	Trees
Unsupervised
K-Means
C-Means
Apriori
Reinforcement
Q-Learning
www.bigdatatrunk.com
The Data Science Process
(DIAPERS)
Define
Problem
Ingest
Data
Analyze
Data
Prepare
Data
Evaluate
Models
Refine
Model Ship It
What data
should I use?
Is it
labeled?
Is data complete, clean,
does it have coverage?
Which
algorithms
should you
use?
What level of
performance
is acceptable?
Deploy the
Model and
make
predictions
What are we
trying to
achieve?
Is it
labeled?
Machine	Learning	
Offerings
38 www.bigdatatrunk.com
www.bigdatatrunk.com
Machine Learning
Offerings
39
Amazon
Machine Learning
Google TensorFlow
Machine Learning
Microsoft Azure
Machine Learning
Spark
Machine Learning
www.bigdatatrunk.com
Machine Learning Using
AI/ML/DL	
41 www.bigdatatrunk.com
AI	v/s	ML	v/s	DL
42 www.bigdatatrunk.com
Artificial
Intelligence
Machine	
Learning
Deep	
Learning
Machine	
Learning	
v/s	
Deep	
Learning
www.bigdatatrunk.com 43
www.bigdatatrunk.com
AI vs ML vs DL
AI ML DL
1950 1980 2006
Driverless car, Alexa Recommendation,
Fraud detection, Image
recognition
Color B/W picture, add
sound to video
Uses ML,DL and
repositories of data
Works with all sizes of
data but needs feature
engineering
Needs large datasets &
compute capability and
takes long time to learn
Linear Regression,
Decision Regression, K
– means Clustering
CNN, ANN
(TensorFlow and Keras)
www.bigdatatrunk.com
Artificial
Intelligence
www.bigdatatrunk.com 45
Artificial	Intelligence	- Types
46 www.bigdatatrunk.com
Artificial
Narrow
Intelligence
Artificial
General
Intelligence
• AI	that	is	good	at	one	specified	task	which	they	
are	trained	on
• Examples	– predicting	home	prices	based	on	
historical	data,	categorize	email	as	SPAM
• Lot	of	buzz	about	the	progress	in	AI,	but	this	is	
only	in	ANI	(Artificial	Narrow	Intelligence)	
• Ultimate	goal	– make	the	computer	smart	or	
smarter	than	the	humans
• AI	that	can	perform	intelligent	tasks	as	humans
• Raises	fears	about	job	loses,	“terminator”	like	
scenarios
• Still	far	from	reaching	the	goal	of	Artificial	
General	Intelligence	(AGI)
World	of	Artificial	Intelligence
47 www.bigdatatrunk.com
AI	is	machines	with	senses
Eye	=	Computer	Vision
Search	&	other	applications
Web	Search
Image	Search
Video	Search
News	Search
Language	&	Speech
Data	is	the	new	
oil.
Memory	and	Knowledge
www.bigdatatrunk.com
AI Offerings
49
Amazon
SageMaker
Google AI Hub
& TensorFlow
Microsoft Cortana &
Azure
Bot
IBM
Watson
Industry	Use		Cases
50 www.bigdatatrunk.com
Customer	Interaction
51 www.bigdatatrunk.com
• Gartner	group	predicts	that	by	2020	over	80%	of	all	
customer	interactions	will	be	handled	by	Artificial	
Intelligence
• Chatbot	site	examples	– written	and	voice
Source:	https://apiumhub.com/tech-blog-barcelona/artificial-intelligence-ecommerce/
E-Commerce	Usage
52 www.bigdatatrunk.com
• Companies	like	Alibaba,	Amazon,	eBay,	etc.	are	using	AI	for	
detection	of	fake	reviews,	chatbots,	product	
recommendations,	managing	big	data,	etc.
Warehouse:	Amazon
53 www.bigdatatrunk.com
Reference
https://www.youtube.com/watch?v=HSA5Bq-1fU4
Health	Care	– Virtual	Dr.	Molly
54 www.bigdatatrunk.com
Reference
https://www.youtube.com/watch?v=AU1nGpOmZpQ
Health	Care
55 www.bigdatatrunk.com
Reference
https://ai.googleblog.com/2018/02/assessing-cardiovascular-risk-factors.html
Ride	Share	– Uber	and	Lyft
56 www.bigdatatrunk.com
• Machine	Learning
• Supply	&	Demand
• Route	Optimization
• Rush	Hour	Pool
• Uber	Eats
57 www.bigdatatrunk.com
AI	By	Industry	Sectors
58 www.bigdatatrunk.com
AI	and	Ethics
59 www.bigdatatrunk.com
www.bigdatatrunk.com
www.bigdatatrunk.com
Summary
Supervised vs Unsupervised01
CCR & DIAPERS Model02
AI vs ML vs DL03
AI and ML Offerings04
Bright Future05
www.bigdatatrunk.com
Demo Time
www.bigdatatrunk.com
g.co/aiexperiments
Thank	You
www.BigDataTrunk.com
For	any	questions	you	can	reach	us	at
Phone– 510	-894-9922
Email	training@bigdatatrunk.com
65 www.bigdatatrunk.com

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