This document provides an overview of machine learning and how it can be used now in business. It discusses how machine learning has reached a tipping point due to advances in computing power, data collection, and algorithms. The document outlines several use cases for machine learning, such as recommendations, sentiment analysis, and predictive analytics. It also addresses common myths about machine learning and how to get started, emphasizing that machine learning capabilities are now readily available through cloud services and open source tools.

1. A first-class ticket to next
generation business.
MACHINE LEARNING

2. Expertise on demand.
Train your ideal decision-making process,
then execute it anytime, anywhere, at any scale.
WHAT WE'RE TALKING ABOUT

3. Fill in the gaps and squash hype around ML,
Build the case for using it now,
And provide easy ways to get started.
TODAY’S GOAL

4. ● Why now?
● Foundation
● Use cases
● ~Technical
● Get started
● Demos
OUR JOURNEY

5. Who thinks machine learning is some kind of voodoo?
( That’s a good thing. )
● We’re not going to dive into the math
● My goal is to show you how easy it is to use
● It’s a tool — just another API
You don't need to understand how
an engine works to drive a car.
KEEP IT SIMPLE

6. ● Software is eating the world and machine learning
is eating the software
● Machine learning (AI) will be the backbone of all
next generation business
“mobile first” => “AI first”
WHY IT'S IMPORTANT

7. Whether you want to:
● Start a new business,
● Enhance an existing business, or
● Get a new job/promotion
Machine learning will give your applications
superpowers ...for now.
(It will be the norm very soon)
WHAT IT CAN DO FOR YOU

8. ● You don’t need a supercomputer
● You don’t need to write a ton of code
● You don’t need to invest massive amounts of time
● You don’t need a data science degree
● You don’t need to be a math whiz
● You don’t need mountains of data
MYTH BUSTING

9. WE’VE HEARD IT BEFORE
Is machine learning hype living up
to expectations this time around?

10. Everything is becoming software
● Limitless computing
● Limitless storage
● Limitless data (IoT = massive need)
● Deep learning
● Targeted machine learning SaaS (easy access)
But, more importantly...
WHY NOW?

11. Because Google says so :)
“Machine learning is not the future. It is now.”
~Google I/O 2016
WHY NOW?
youtube.com/watch?v=3dXQxSI3XDY

12. Massive strides in the past year
Just in the past few months…
● Google open sources natural language processing
platform
● Amazon open sources deep learning platform
● Google announces quantum computing works
● IBM offers access to quantum computer
● Google’s DeepMind beats Go champion
WHAT’S NEW

13. WILL IT STICK THIS TIME?
The Internet gave us big data (greater need)
The cloud gave us massive computing (more horsepower)
And it’s getting much, much bigger…

14. BIG DATAx

15. MASSIVE COMPUTINGx
100 million times faster...?
“I would predict that in 10 years there’s nothing but
quantum machine learning”
~Hartman Nevet
Head of Google’s Quantum AI Lab
via: technologyreview.com
via: researchgate.net

16. ON A PATH TO UBIQUITY
“The most profound technologies are those that
disappear. They weave themselves into the fabric of
everyday life until they are indistinguishable from it.”
~Mark Weiser
Scientific American, 1991

17. IN JUST 4 YEARS
Predicted for 2020...
● 13% of US households own consumer robots 1
(robotics)
● 30% of new cars will have a self-driving mode 2
(auto)
● 70% of mobile users access devices via biometrics 2
(security)
● We interact with 150+ smart devices (IoT) every day 2
(lifestyle)
All are underpinned by machine learning
1
roboticstrends.com/article/13_of_us_households_to_own_consumer_robots_by_2020
2
weforum.org/agenda/2015/02/5-predictions-for-technology-in-2020

18. ADDING FUEL TO THE FIRE
Think global.
tractica.com/newsroom/press-releases/artificial-intelligence-for-enterprise-applications-to-
reach-11-1-billion-in-market-value-by-2024

19. HOW I GOT STARTED
Apache
Mahout
Decision Forest
Behavior
prediction
Suite of
mobile apps
Determine the most relevant
(highest-converting) sales offer to present to
each individual user — and the best
(highest-converting) time to present it.

20. Will the current user buy “Madden NFL” right now?
WHAT IS A DECISION FOREST?
is male?
is age
> 16?
is Y app
installed?
is X app
installed?
end
has used >
30 days?
was X
function
used?
was Y
function
used?
no
yes
no
yes
no
yes
no
yes
end
(better ways to do this now)
no
yes
end
do it

21. FOUNDATION
Exploring the basics.

22. “An algorithm
that can learn
from data without
relying on
rules-based
programming.”
WHAT IS MACHINE LEARNING?
analyticsvidhya.com/blog/2015/07/difference-machine-learning-statistical-modeling

23. Training your computer to do stuff,
just like you would train a pet.
IN OTHER WORDS...

24. SIMILAR TO HOW WE LEARN
Data System Output
Model
Question Answer
Life experience
Emotions
Mindset
Training data
Algorithm
Perspective

25. ● Model — The reference data pattern (decision-making stuff)
● Algorithm — Process the computer uses to learn the model
(perspective)
● Training — Building the model from historical data (life
experience)
○ Supervised learning — Labeled training data
○ Unsupervised learning — Unlabeled training data
○ Reinforcement learning — Reward-based training
● Feature — Points of differentiation in the data
MAJOR COMPONENTS
cse.unsw.edu.au/~billw/mldict.html

26. ENDLESS ALGORITHMS

27. Different for each algorithm & platform
For Amazon Machine Learning (logistic regression)…
● Binary (Yes or no, Actionable or non-actionable)
● Pick from list (Is this tweet a question, complaint, or
praise?)
● Number (How much will this house sell for?)
Sky's the limit on how you can apply these
WHAT IS THE OUTPUT?

28. IN THE WILD
Recommender
(pick from list)
Classifier
(binary)
Visual
recognition
(deep learning)

29. “Features”
How would you teach a
child to recognize the
differences?
● Distance between eyes
● Width of nose
● Shape of cheekbones
HOW DOES IT CLASSIFY?

30. “Probability”
Each potential
answer gets a
numeric
probability
calculated for it.
Higher
probability
means greater
confidence.
HOW DOES IT MAKE DECISIONS?

31. A few examples of ML fueling
growth in big business.
USE CASES

32. Understand & answer
SEARCH RESULTS
( ibm.com/smarterplanet/us/en/ibmwatson/developercloud/concept-insights.html )

33. PRODUCT RECOMMENDATIONS
techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html
templates.prediction.io/PredictionIO/template-scala-parallel-universal-recommendation

34. SENTIMENT ANALYSIS
keyhole.co
ibm.com/smarterplanet/us/en/ibmwatson/developercloud/tone-analyzer.html
templates.prediction.io/pawel-n/template-scala-cml-sentiment

35. SPEECH RECOGNITION
cloud.google.com/speech
developer.amazon.com/public/solutions/alexa
github.com/tensorflow/models/tree/master/syntaxnet (Parsey McParseface)

36. Assembled by
machine learning
360° PHOTOS
bgr.com cloud.google.com/vision

37. TRANSLATION

38. AUTOMATED CAPTIONS
“A group of young
people playing a
game of frisbee.”
Great example of
deep learning —
understanding the
context of an image.
io9.gizmodo.com/computers-wrote-the-caption-for-this-photograph-and-ch-1660450610

39. ( I believe every business will need
these 2 systems moving forward. )
COMPOUNDING
FUNCTIONALITY

40. Speech
to Text
Sentiment
Analysis
Actionable
Analysis
Customer
Support
PREDICTIVE ENGAGEMENT
Customer
support call
recordings
Convert audio
into text
Analyze for
mood keywords
Determine if
response is required
Reach out to
customer/prospect
Blog & community comments
Social media mentions
Press & blog coverage
Customer support chat
Product reviews
Inbound emails
[ IBM Watson Speech to Text ] [ IBM Watson Tone Analyzer ] [ IBM Watson AlchemyLanguage ]

41. Behavior
Prediction
Interest
Tracking
PREDICTIVE PERSONALIZATION
Pages & content they’ve visited
Emails they’ve opened/clicked
Resources they’ve used/downloaded
Products they’ve viewed/wishlisted/bought
Searches they’ve made
Blog
Store
Find patterns Determine what they want to
see/do/buy next (and when)
Days/time they’re active App
Search
Devices they’ve used (& geo location)
Email
Social
• Recommended posts
• Recommended products
• Delivery day/time
• Dynamic content
• Related posts
• Sales offers
• Related products
• Cross/up sell
• Dynamic pricing
• Dynamic content
• Sales offers
• Functionality
• Query suggestions
• Results ranking
• Sales offers
• Content curation
• Delivery day/time
• Retweet/reshare
Tribe
• Recommended topics
• Topic curation
• Member introductions[ Amazon Machine Learning ]
[ Amazon Machine Learning ]

42. Moving into the technical details.
A BIT DEEPER

43. A many-layered Artificial Neural Network (~self-learning)
WHAT IS DEEP LEARNING?
“deep”
cs231n.github.io/neural-networks-1
“shallow”

44. (SIMPLE) NEURAL NETWORK
Each layer performs a
discrete function
Each neuron takes in
multiple inputs
≥ 1 input
neurons
≥ 1 output
neurons
≥ 1 hidden layers
Output “fires” if all
weighted inputs sum
to a set “threshold”
Each connection applies a
“weighted” influence on
the receiving neuron
Layers build on each other
(iterative)
Each input can
be a separate
“feature”

45. HOW MUCH IS A HOUSE WORTH?
Decisions based on combinations.
3 bedrooms
37 years old
1450 ft2
$191,172
Is it “old” or “historic?”
Is it “small” or “open floor plan?”
$32,108 per bedroom
$64,251 per acre
Need a lower weight for “old”
Apply initial
abstractions
Set values

46. ● Vanilla Neural Network — nothing fancy
● Convolutional Neural Network — inspired by visual
cortex
● Deep Belief Network — undirected connections
● Recurrent Neural Network — multi-pass
MANY DIFFERENT FLAVORS

47. ● R
● Python
● Matlab/Octave
● Java
● C / C++
kdnuggets.com/2016/06/r-python-top-analytics-data-mining-data-science-software.html
POPULAR LANGUAGES

48. ● Amazon Machine Learning
● Google Prediction API*
● Google Cloud Machine Learning
● Microsoft Azure Machine Learning
● IBM Watson Machine Learning
● DiffBot
● Alibaba Cloud DT PAI
SaaS OPTIONS

49. ● TensorFlow *
● Amazon DSSTNE *
● H2O *
● PredictionIO
● Apache Mahout
● Scikit Learn
● Caffe *
OPEN SOURCE OPTIONS
● Microsoft CNTK *
● Torch *
● Theano *
● MXnet *
● Chainer *
● Keras *
● Neon *
* Deep learning

50. How to get the ball rolling.
GET STARTED

51. ● archive.ics.uci.edu/ml
● deeplearning.net/datasets
● mldata.org
● grouplens.org/datasets
● cs.toronto.edu/~kriz/cifar.html
● cs.cornell.edu/people/pabo/movie-review-data
● yann.lecun.com/exdb/mnist (handwriting)
● kdnuggets.com/datasets/index.html (long list)
● image-net.org (competition)
OPEN SOURCE DATASETS

52. ● playground.tensorflow.org (neural network demo)
● cs.stanford.edu/people/karpathy/convnetjs
● github.com/awslabs/machine-learning-samples
● ibm.com/smarterplanet/us/en/ibmwatson/develop
ercloud/starter-kits.html
● templates.prediction.io
EASY STARTING POINTS

53. Start now
● It’s here, now
● It’s evolving exponentially
● Build “AI-First”
RECAP

54. Let’s see some action.
DEMOS

55. ● AlchemyLanguage
● Dialog
● Natural Language Classifier
● Personality Insights
● Relationship Extraction
● Tradeoff Analytics
ibm.com/smarterplanet/us/en/ibmwatson/developercloud/services-catalog.html
IBM WATSON

56. UNLEASH YOUR BUSINESS
EMBRACE EXPONENTIAL
10xnation.com