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Agenda
Machine Learning Doomsday
ML vs DL vs AI?
Marketing Use Cases
Models & Use Cases
Tools For Marketers
Wrapping Up
Real World Examples
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Singularity is Considered a Very Real Theory
Ray Kurzweil believes that we will achieve singularity by 2045.
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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.
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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.
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.
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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.
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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
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Experts Disagree on When General Intelligence Will Happen
The primary i ssue keeping this from happening is computing power.
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Experts Disagree on When General Intelligence Will Happen
The primary i ssue keeping this from happening is computing power.
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Accelerating Moore’s Law
Google has been working on quantum computing to accelerate Moore’s Law
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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.”
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Supervised Learning
The machine looks for patterns that match the labeled data that you provide and classifies new data based on that.
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Unsupervised Learning
The machine identifies patterns in the data and creates clusters based on what it finds.
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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.”
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.
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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.
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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.
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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.
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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.
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Clustering & Classifying Keywords
http://ipullrank.com/clustering-vs-classification-speed-keyword-research/
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Clustering & Classifying Keywords
http://ipullrank.com/clustering-vs-classification-speed-keyword-research/
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Computer Vision
There are services that leverage machine learning and computer vision to identify objects in pictures.
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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.
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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).
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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
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Adwords Scripts
http://searchengineland.com/machine-learning-adwords-scripts-google-prediction-api-217936
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Models & Use Cases
Random ForestLead Qualification
Logistic RegressionCustomer ChurnPrediction
Decision TreesCustomer ChurnPrediction
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Models & Use Cases (Cont’d)
Support Vector MachinesTextCategorization
AprioriMarket Basket Analysis(Amazon)
Naïve BayesSentiment AnalysisRecommendation SystemsSpam Classification
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K-Fold Cross Validation
Try out a model and validate it using k-fold cross validation.
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How to Choose a Machine Learning Model
https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice
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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.
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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.
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Beeswax Bidder-as-a-Service
Beeswax allows you to set up custom models to run your Display RTB campaigns.
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mTurk - Labeling Data for Supervised Learning
Exploratory Data Analysis helps identifying general patterns in the data and serve as initial explorations of correlations.
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API.AI Generating Chatbots
Exploratory Data Analysis helps identifying general patterns in the data and serve as initial explorations of correlations.
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NanoRep
Exploratory Data Analysis helps identifying general patterns in the data and serve as initial explorations of correlations.
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MonkeyLearn & Orange
We wi ll primarily ta lk about MonkeyLearn and Orange as two tools marketers can use to do machine learning right now.
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These Examples Use the Iris Petals Dataset
https://archive.ics.uci.edu/ml/datasets/Iris
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Exploratory Data Analysis
Exploratory Data Analysis helps identifying general patterns in the data and serve as initial explorations of correlations.
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Exploratory Data Analysis: Scatter Plot
Two-dimensional scatter plot shows class density.
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Exploratory Data Analysis: Distributions
Compare the distributions of different type of i ris.
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Classification Tree
Observe the pattern across nodes to discover important variables.
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Import Text Mining Add-on
Install the free text mining add-on in order to use
Orange’s text mining capabilities.
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Load and Preprocess Dataset
Preprocess text to find meaningful words only.
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Word Cloud
Using the word cloud, we can determine the frequency of keywords in the list.
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Hierarchical Clustering
We can use this to determine similarity in the corpus or dataset.
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Hierarchical Clustering
Once we understand the hierarchy, we can dig into the documents in the viewer to see how the model has organized them.
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SVM: Linear vs. Non-linear
Linear SVM often outperforms non-linear in text classification.
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Confusion Matrix: Non-linear SVM
Send misclassified samples to corpus viewer.
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Logistic Regression: Ridge vs. Lasso
Logistic regression with l2 penalty achieve higher accuracy.
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Compare Models
Linear SVM and logistic regression outperform the other two models.
IPULLRANK.COM @ IPULLRANKMonkey Learn is a text mining cloud platform.
MonkeyLearn Now Works with Google Sheets
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MY NAME IS MIKE KING
Razorfish, Publicis Modem alum
Full Stack Developer
Full Stack Marketer
Moz Associate
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We Do These Things
Content
StrategySEO Paid Media Machine
Learning
Marketing
Automation
Measurement
& Optimization
IPULLRANKhttp://ipullrank.com
THANK YOU
Michael King
Managing Director
(347) [email protected]
02/21/2017