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Big	Data	and	Machine	
Learning	for	businesses
By	Abdul	Wahid
Who	AM	I
Abdul	Wahid
CEO	at	GleeTech (http://gleetech.com)
• Started	working	as	a	Software	Engineer	in	2005.
• PhD	(Computer	Science).
• 2016,	Victoria	University	of	Wellington
• Help	people	to	develop	awesome	digital	products.
• Passionate	about
• Big	Data
• Machine	Learning
• Tech	Startups/Businesses
• Email:abdul@gleetech.com
• Personal	site:	http://awahid.net
What	is	Big	Data?
Big	Data
• Additional	Dimentions
• Complexity
• Multiple	sources	and	data	streams
• Variability
• Unpredictable	Data	flows.
• Social	media	trending.
Data	is	the	new	Oil	of	the	Digital	
Economy
Wired	2014
Why	Big	Data	is	important
• Data	contains	information.
• Information	leads	to	insights.
• Insights	helps	in	making	better	decisions.
Examples	- Using	data
• Weather	Data
• Utility	&	Manufacturing	companies	
• Transport	&	Tourism
• News,	Accidents,	Census
• Insurance	Companies	
• Survey,	Blogs,	Comments,	Tweets
• Marketing	Analysis
• Customer	Segmentation
Examples	- Benefits	of	data
• Sports
• Strategies,	Data	driven	analytics
• Entertainment
• Users	opinions,	Sentiments
• Retail
• Consumer	behavior
• Health	Care
• Patient/symptom	analysis
• Financial	Institutions
• Fraud	Detection
How	to	derive	insights	from	data?
Machine	Learning?
“Field	of	study	that	gives	computers	the	ability	to	learn	without	being	explicitly	
programmed.”	Arthur	Samuel	(1959).
https://www.linkedin.com/pulse/business-intelligence-its-relationship-big-data-geekstyle
Top	Algorithms
• Supervised	Learning
• Decision	Trees
• Naïve	Bayes	Classification
• Ordinary	Least	Squares	Regression
• Logistic	Regression
• Support	Vector	Machines
• Ensemble	Methods
• Unsupervised	Learning
• Clustering	Algorithms	
• Centroid-based	algorithms
• Connectivity-based	algorithms
• Density-based	algorithms
• Probabilistic
• Dimensionality	Reduction
• Neural	networks	/	Deep	Learning
• Principal	Component	Analysis
• Singular	Value	Decomposition
• Independent	Component	Analysis
Example
• We	want	some	hypothesis	h	that	predicts	whether	we	will	be	paid	back
1. +a,	-c,	+i,	+e,	+o,	+u:	Y
2. -a,	+c,	-i,	+e,	-o,	-u:	N
3. +a,	-c,	+i,	-e,	-o,	-u:	Y
4. -a,	-c,	+i,	+e,	-o,	-u:	Y
5. -a,	+c,	+i,	-e,	-o,	-u:	N
6. -a,	-c,	+i,	-e,	-o,	+u:	Y
7. +a,	-c,	-i,	-e,	+o,	-u:	N
8. +a,	+c,	+i,	-e,	+o,	-u:	N
• Lots	of	possible	hypotheses:	will	be	paid	back	if…
• Income	is	high	(wrong	on	2	occasions	in	training	data)
• Income	is	high	and	no	Criminal	record	(always	right	in	training	data)
• (Address	is	known	AND	((NOT	Old)	OR	Unemployed))	OR	((NOT	Address	is	known)	AND	(NOT	
Criminal	Record))	(always	right	in	training	data)
• Which	one	seems	best?		Anything	better?
Decision	trees
high	Income?
yes no
NO
yes no
NO
Criminal	record?
YES
Constructing	a	decision	
tree,	one	step	at	a	time address?
yes no
+a,	-c,	+i,	+e,	+o,	+u:	Y
-a,	+c,	-i,	+e,	-o,	-u:	N
+a,	-c,	+i,	-e,	-o,	-u:	Y
-a,	-c,	+i,	+e,	-o,	-u:	Y
-a,	+c,	+i,	-e,	-o,	-u:	N
-a,	-c,	+i,	-e,	-o,	+u:	Y
+a,	-c,	-i,	-e,	+o,	-u:	N
+a,	+c,	+i,	-e,	+o,	-u:	N
-a,	+c,	-i,	+e,	-o,	-u:	N
-a,	-c,	+i,	+e,	-o,	-u:	Y
-a,	+c,	+i,	-e,	-o,	-u:	N
-a,	-c,	+i,	-e,	-o,	+u:	Y
+a,	-c,	+i,	+e,	+o,	+u:	Y
+a,	-c,	+i,	-e,	-o,	-u:	Y
+a,	-c,	-i,	-e,	+o,	-u:	N
+a,	+c,	+i,	-e,	+o,	-u:	N
criminal? criminal?
-a,	+c,	-i,	+e,	-o,	-u:	N
-a,	+c,	+i,	-e,	-o,	-u:	N
-a,	-c,	+i,	+e,	-o,	-u:	Y
-a,	-c,	+i,	-e,	-o,	+u:	Y
+a,	-c,	+i,	+e,	+o,	+u:	Y
+a,	-c,	+i,	-e,	-o,	-u:	Y
+a,	-c,	-i,	-e,	+o,	-u:	N
+a,	+c,	+i,	-e,	+o,	-u:	N
income?
+a,	-c,	+i,	+e,	+o,	+u:	Y
+a,	-c,	+i,	-e,	-o,	-u:	Y
+a,	-c,	-i,	-e,	+o,	-u:	N
yes no
yes no
yes no Address	was	
maybe	not	the	
best	attribute	to	
start	with…
Starting	with	a	different	attribute
• Seems	like	a	much	better	starting	point	than	address
• Each	node	almost	completely	uniform
• Almost	completely	predicts	whether	we	will	be	paid	back
yes no
+a,	-c,	+i,	+e,	+o,	+u:	Y
-a,	+c,	-i,	+e,	-o,	-u:	N
+a,	-c,	+i,	-e,	-o,	-u:	Y
-a,	-c,	+i,	+e,	-o,	-u:	Y
-a,	+c,	+i,	-e,	-o,	-u:	N
-a,	-c,	+i,	-e,	-o,	+u:	Y
+a,	-c,	-i,	-e,	+o,	-u:	N
+a,	+c,	+i,	-e,	+o,	-u:	N
criminal?
-a,	+c,	-i,	+e,	-o,	-u:	N
-a,	+c,	+i,	-e,	-o,	-u:	N
+a,	+c,	+i,	-e,	+o,	-u:	N
+a,	-c,	+i,	+e,	+o,	+u:	Y
+a,	-c,	+i,	-e,	-o,	-u:	Y
-a,	-c,	+i,	+e,	-o,	-u:	Y
-a,	-c,	+i,	-e,	-o,	+u:	Y
+a,	-c,	-i,	-e,	+o,	-u:	N
Naïve	Bayes	Classification
Linear	Regression
• The	task	of	fitting	a	
straight	line	through	a	
set	of	points
Logistic	Regression
• Measures	the	relationship	
between	the	categorical	
dependent	variable	and	
one	or	more	independent	
variables
• Credit	Scoring
• Measuring	the	success	
rates	of	marketing	
campaigns
• Predicting	the	revenues	of	
a	certain	product
Support	Vector	Machine
• Binary	classification	algorithm
• SVM	generates	a	(N — 1)	
dimensional	hyperlane to	
separate	those	points	into	2	
groups.
Ensemble	Methods
Clustering	Algorithms
Principle	Component	Analysis
Singular	Value	Decomposition
• PCA	is	actually	a	simple	
application	of	SVD
Independent	Component	Analysis
• Revealing	hidden	factors	that	
underlie	sets	of	random	
variables.
• data	variables	are	assumed	to	
be	linear	mixtures	of	some	
unknown	latent	variables,	and	
the	mixing	system	is	also	
unknown.
• The	latent	variables	are	
assumed	non-gaussian and	
mutually	independent.
• Images,	Documents
https://thinkgrowth.org/artificial-intelligence-and-you-demystifying-the-technology-landscape-21d4d34fbb10
Conclusions
• Data	is	nothing	without	insights
• Machine	learning	is	the	key	for	deriving	insights	from	data	
• Big	Data	and	Machine	Learning	has	a	huge	potential
• Mobile	First	to	AI	First	Approach
Next	Steps
• Machine	Learning	Course
• https://www.coursera.org/learn/machine-learning
• Big	Data	Course
• https://www.coursera.org/specializations/big-data
• Must	know	Machine	Learning	Algorithms
• http://www.kdnuggets.com/2016/08/10-algorithms-machine-learning-
engineers.html
• Machine	Learning	Startups
• https://angel.co/machine-learning
• Recent	Cool	Startups
• http://www.informationweek.com/big-data/software-platforms/10-cool-machine-
learning-startups-to-watch/d/d-id/1326571
References
• https://www.slideshare.net/nasrinhussain1/big-data-ppt-
31616290?from_action=save
• http://www.kdnuggets.com/2016/08/10-algorithms-machine-learning-engineers.html
• https://www.slideshare.net/larsga/introduction-to-big-datamachine-learning/40-
Top_10_machine_learning_algs1
• https://www.google.com.pk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0ahUK
Ewjx0_2Q5b7TAhXFPRQKHfiNCV8QFggmMAE&url=http%3A%2F%2Fnlp.cs.rpi.edu%2Fco
urse%2Fspring16%2Flecture10.pptx&usg=AFQjCNFFg6FbId7hqMj1lwbufcfPvb7Wmw
• https://thinkgrowth.org/artificial-intelligence-and-you-demystifying-the-technology-
landscape-21d4d34fbb10
• https://www.linkedin.com/pulse/business-intelligence-its-relationship-big-data-geekstyle

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