Machine Learning Agenda and Use Cases

Managing Director
347-391-4266
mike@ipullrank.com
MACHINE DOING
12/01/2016
Michael King

IPULLRANK.COM @ IPULLRANK
Agenda
Machine Learning
Doomsday
ML vs DL vs AI?
Marketing Use Cases
Models & Use Cases
Tools For Marketers
Wrapping Up
Real World Examples

Machine Learning
Doomsday
Subtitlegoes here.

Smart People are Scared of
Artificial Intelligence

Really Smart People…

It’s All Olivia Pope’s dad’s fault
Either the Robots Enslave Us

Or Evolve to a Point We Can’t
Understand

Singularity is Considered a Very Real Theory
Ray Kurzweil believes that we will achieve singularity by 2045.

No matter what, it’s all Olivia
Pope’sdad’sfault

No Matter What Larry & Sergey are All Set Though

Don’t Forget Isaac Asimov

Machine Learning Can Write Copy For you
There is a sub-field of artificial intelligence called Natural LanguageGeneration that has made the concept of
content spinning a lot more viable and hasbeen used for sports recaps and financial reports.

But It Can Also Fuck It Up

AI Is Gonna Steal Your Job?
One of the more common fears of middle America around the idea of artificial intelligence is that robots will replace
humans in their jobs.

Obama Had Some Measured
Thoughts On His Way Out

The real fear of machine learning
and artificial intelligence should be
its ability to reflect and amplify our
biases and the lack of diversity of
the people creating it.

In the meantime though, it can get you a date
(h/t @goutaste)

And you can go there in a self-driving Uber Lyft

Machine Learning vs. Deep
Learning vs. AI
The Core Concepts

They Are Not the Same Thing

AI is Comprised of Many Disciplines
Deep Learning is a subset of Machine Learning is a subset of Artificial
Intelligence.
AI many branches of which machine learning is a core branch that we can execute.

Artificial Intelligence as it is
represented in sci-fi is
“general” artificial
intelligence. What we have
achieved so far is “narrow”
artificial intelligence.

Types of Artificial Intelligence Explained
Using “The Lawnmower Man”
Narrow Artificial Intelligence
Machines that can do a specific
task or series of tasks
exceedinglywell and very
efficiently.
General Artificial Intelligence
A machine that is as smart as a
human in that it can take in new
situations and make decisions.
Artificial Superintelligence
A machine that is potentially
orders of magnitude smarte
than a human in all categorie
simultaneously

Experts Disagree on When General
Intelligence Will Happen
The primaryissue keeping this from happening is computingpower.

Accelerating Moore’s Law
Google has been working on quantum computing to accelerate Moore’s
Law

100mm
times faster thana classicalcomputerby
using a D-Wavequantumcomputer
NewScientist.com

Ok. So, What Is Machine Learning?
“Machine learning is a type of
artificial intelligence that
provides computers with the
ability to learn without being
explicitly programmed.”

Supervised
Learning
The machine looks for patterns that match the labeled data that you provide and classifies new data based on that.

Unsupervised
Learning
The machine identifies patterns in the data and creates clusters based on what it finds.

Reinforcement Learning
With reinforcement learning, the model is continually trained based on new data thereby improving the classifier’s
ability to perform.

And Deep Learning?
“Deep Learning is a subfield of
machine learning concerned
with algorithms inspired by the
structure and function of the
brain called artificial neural
networks.”

That’s not what we’re
talking about today.

Machine Learning vs. Statistics
Machine Learning learns from data without
relying on rules-based programming,
statistical modeling identifies relationships in
the form of mathematical equations.

All Values vs. Linear
Representation
Machine Learning examines
all potential values based on
probability whereas statistics
looks for a linear function to
describe the trend.

Machine Learning is the “Growth Hacking”
of the Statistics World
However, in some ways machine learning and statistics are so similar that many statisticians just feel as though
machine learning is just a rebranding of what they do much like “growth hacking” is just a rebranding of marketing.

The Machine Learning Process
GET & PREPARE YOUR
DATA
You identify and clean your
dataset in preparation for
solving the machine learning
problem
CHOOSEYOUR MODEL
TRAIN YOUR CLASSIFIER
You chose the algorithm or
model that you believe will
yield the best results then run
it in order to train your
classifier.
SCORE AND EVALUATE
You score the accuracy and
precision of the classifier and
test it against other
algorithms to see what
performs best.
PREDICT OR IDENTIFY
OUTCOMES
Once you are happy with the
results, you use the classifier
moving forward to make
conclusions about new data.

Car Rental
Example
This is an example of how
you could predict the
demand of cars for a car
rental company. It follows
the same framework.

Marketing Use Cases
Subtitlegoes here.

Predictive Analytics

Marketing Campaign Performance Prediction

Customer Churn
Prediction

Personalization

Customer
Segmentation

Natural Language
Processing

Clustering & Classifying Keywords
http://ipullrank.com/clustering-vs-classification-speed-keyword-research/

Follow Vicky Qian
@vickyqian24

Sentiment Analysis

Natural Language Generation

Computer Vision
There are servicesthat leverage machine learningandcomputervisionto identifyobjects inpictures.

Chatbots

Training Chatbots
Training chatbots is similar to training ML classifiers inthat you take a knowledge base andrun it throughNLPthentune it with
regard to conversations.

Real World Examples
Some things we workon

We Re-Ranked the Inc. 500

Follow Up Blog
Content

Company-level Report

Retargeting Ads

Super-specific Retargeting Ads

We Built a Simple Marketing Automation System

Each Contact Has a Unique URL

Integrates with Reply

LinkedIn Sales Navigator

Prospectify for finding Emails Quickly

Salesperson Writes Mail Merge Templates

42% Open Rate!

The Methodology is
the Machine Learning
Part
We took all available
domain-level link features
for the Searchmetrics losers
and winners and figured out
(5-fold cross validation,
random forest and lasso)
which ones correlated best
with the results and then
used that model to re-rank
the Inc. 500. (I probably
shoulda asked Marcus for
more data, but whatever).

Methodology behind the Vector Report
We broke it into two types of machine learning questions. Classification and Logistic Regression to
predict the probability of continued visibility in Organic Search.
Goal: identify SEO
winners and losers and
predict a site’s
performance in SEO
Classification
Random Forest
Gradient Boosting
Machine
Support Vector
Machine
Logistic Regression Regularization

Have You Met @tomcritchlowbot?

The Bot wit the Solid Delivery.

This is Twitter bot built from Markov
Chains

Adwords Scripts
http://searchengineland.com/machine-learning-adwords-scripts-google-prediction-api-217936

Programmatic Display
Lookalike moderlign

Models
Types of Models when you should Use Them

There are Tons of Different Models
Your best bet is to test and learn.

Seriously Tonnnnnns

The Uses of Each Type Are Difficult to Memorize

Models & Use
Cases
Random Forest
Lead Qualification
Logistic Regression
Customer Churn
Prediction
Decision Trees
Customer Churn
Prediction

Models & Use Cases (Cont’d)
Support Vector
Machines
Text
Categorization
Apriori
Market Basket Analysis
(Amazon)
Naïve Bayes
Sentiment Analysis
RecommendationSystems
Spam Classification

K-Fold Cross Validation
Try out a model and validate it using k-fold cross validation.

How to Choose a Machine Learning Model
https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice

Tools for Marketers
Subtitlegoes here.

yHat Science Ops
Open source machine learning and data visualization for novice and expert.
Most machine learning is done in R or Python, but those are programming languages.

yHat Science Ops
yHat allows you to deploy machinelearning modelsasREST APIsthat can then beintegrated with your site like any
other API.

Beeswax Bidder-as-a-Service
Beeswax allows you to set up custom modelsto run your Display RTB campaigns.

Those are tools that
allow marketers to
take control with a
data scientist.

mTurk - Labeling Data for Supervised Learning
ExploratoryData Analysis helps identifying general patterns in the data andserve as initial explorations of correlations.

API.AI Generating Chatbots

NanoRep

MonkeyLearn & Orange
We will primarilytalk aboutMonkeyLearn and Orange as two tools marketers can use to do machine learning right now.

These Examples Use the Iris Petals Dataset
https://archive.ics.uci.edu/ml/datasets/Iris

Exploratory Data Analysis

Exploratory Data Analysis: Scatter Plot
Two-dimensional scatterplot shows class density.

Exploratory Data Analysis: Distributions
Compare the distributions ofdifferent type ofiris.

Classification Tree
Observe the pattern across nodesto discoverimportant variables.

Predictive Text Classification

Import Text
Mining Add-on
Install the free text mining
add-on in order to use
Orange’s text mining
capabilities.

Load and Preprocess Dataset
Preprocesstext to findmeaningful words only.

Word Cloud
Using the wordcloud, we candetermine the frequencyof keywords in the list.

Hierarchical Clustering
We can use this to determine similarityin the corpus or dataset.

Hierarchical Clustering
Once we understandthe hierarchy, we can digintothe documents in the viewerto see howthe model has organizedthem.

Classification

SVM: Linear vs. Non-linear
LinearSVMoftenoutperforms non-linearin text classification.

Confusion Matrix: Non-linear SVM
Send misclassifiedsamples to corpus viewer.

Nearest Neighbors

Logistic Regression: Ridge vs. Lasso
Logistic regression withl2 penaltyachieve higheraccuracy.

Confusion Matrix: Lasso
Send misclassifiedsamples to corpus viewer.

Naive Bayes

Compare Models
LinearSVMandlogistic regression outperform the othertwomodels.

Prediction
Predict with winningclassifiers.

Prediction
SVMand logistic regressionall hit 100% accuracy.

IPULLRANK.COM @ IPULLRANKMonkeyLearn is a text miningcloudplatform.

IPULLRANK.COM @ IPULLRANKMonkeyLearn is a text miningcloudplatform.
MonkeyLearn Now Works with Google Sheets

Monkey Learn: Train Category Tree

Monkey Learn: Tree Parameters

Monkey Learn: Classify with Category Tree

Codementor

Experfy

Kaggle

Wrapping Up
Who am I and where am I from?

I’M
#ZORASDAD
First and foremost.

MY NAME IS
MIKE KING
Razorfish, Publicis Modem alum
Full Stack Developer
Full Stack Marketer
Moz Associate

I Run a Better Marketing Agency Called iPullRank

We Do These Things
Content
Strategy
SEO Paid Media Machine
Learning
Marketing
Automation
Measurement
& Optimization

IPULLRANK
http://ipullrank.com
THANK YOU
Michael King
Managing Director
(347) 391-4266
mike@ipullrank.com
02/21/2017

Machine Learning Agenda and Use Cases

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (20)

Similaire à Machine Learning Agenda and Use Cases

Similaire à Machine Learning Agenda and Use Cases (20)

Plus de Distilled

Plus de Distilled (20)

Dernier

Dernier (20)

Machine Learning Agenda and Use Cases