SearchLove San Diego 2017 | Michael King | Machine Doing

Managing Director

347-391-4266

[email protected]

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

Subtitle goes 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 Kill Us All

Or Evolve to a Point We Can’t Understand

Or we achieve singularity


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’s dad’s fault


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 Language Generation that has made the concept of content spinning a lot more viable and has been 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 IntelligenceMachines that can do a specific task or series of tasks exceedingly well and very efficiently.

General Artificial IntelligenceA machine that is as smart as a human in that it can take in new situations and make decisions.

Artificial SuperintelligenceA machine that is potentially orders of magnitude smarter than a human in all categories simultaneously


Experts Disagree on When General Intelligence Will Happen

The primary i ssue keeping this from happening is computing power.


Accelerating Moore’s Law

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


100mmtimes faster than a classical computer by

using a D-Wave quantum computer

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

CHOOSE YOUR 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

Subtitle goes 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 services that leverage machine learning and computer vision to identify objects in pictures.


Chatbots


Training Chatbots

Tra ining chatbots is similar to training ML classifiers in that you take a knowledge base and run it through NLP then tune i t with regard to conversations.

Real World Examples

Some things we work on


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!

732 Leads


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 ForestLead Qualification

Logistic RegressionCustomer ChurnPrediction

Decision TreesCustomer ChurnPrediction


Models & Use Cases (Cont’d)

Support Vector MachinesTextCategorization

AprioriMarket Basket Analysis(Amazon)

Naïve BayesSentiment AnalysisRecommendation SystemsSpam 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

Subtitle goes 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 machine learning models as REST APIs that can then be integrated with your site like any other API.


Beeswax Bidder-as-a-Service

Beeswax allows you to set up custom models to run your Display RTB campaigns.

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


mTurk - Labeling Data for Supervised Learning

Exploratory Data Analysis helps identifying general patterns in the data and serve as initial explorations of correlations.


API.AI Generating Chatbots



NanoRep



MonkeyLearn & Orange

We wi ll primarily ta lk about MonkeyLearn 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 scatter plot shows class density.


Exploratory Data Analysis: Distributions

Compare the distributions of different type of i ris.


Classification Tree

Observe the pattern across nodes to discover important 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

Preprocess text to find meaningful words only.


Word Cloud

Using the word cloud, we can determine the frequency of keywords in the list.


Hierarchical Clustering

We can use this to determine similarity in the corpus or dataset.


Hierarchical Clustering

Once we understand the hierarchy, we can dig into the documents in the viewer to see how the model has organized them.


Classification


SVM: Linear vs. Non-linear

Linear SVM often outperforms non-linear in text classification.


Confusion Matrix: Non-linear SVM

Send misclassified samples to corpus viewer.


Nearest Neighbors


Logistic Regression: Ridge vs. Lasso

Logistic regression with l2 penalty achieve higher accuracy.


Confusion Matrix: Lasso

Send misclassified samples to corpus viewer.


Naive Bayes


Compare Models

Linear SVM and logistic regression outperform the other two models.


Prediction

Predict with winning classifiers.


Prediction

SVM and logistic regression all hit 100% accuracy.

IPULLRANK.COM @ IPULLRANKMonkey Learn is a text mining cloud platform.

IPULLRANK.COM @ IPULLRANKMonkey Learn is a text mining cloud platform.

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

StrategySEO Paid Media Machine

Learning

Marketing

Automation

Measurement

& Optimization

THAT’S ALL I’VE GOT

IPULLRANKhttp://ipullrank.com

THANK YOU

Michael King

Managing Director

(347) [email protected]

02/21/2017

SearchLove San Diego 2017 | Michael King | Machine Doing

Marketing

Transcript of SearchLove San Diego 2017 | Michael King | Machine Doing