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SearchLove San Diego 2017 | Michael King | Machine Doing

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Machine learning is a concept that has is turning science fiction into real technology today. However, the applications of this technology seem daunting to marketers. In this talk, Mike King shares how marketers can take advantage of machine learning using ready made tools and tactics without knowing how to code.

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SearchLove San Diego 2017 | Michael King | Machine Doing

  1. 1. Managing Director 347-391-4266 mike@ipullrank.com MACHINE DOING 12/01/2016 Michael King
  2. 2. 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
  3. 3. Machine Learning Doomsday Subtitlegoes here.
  4. 4. IPULLRANK.COM @ IPULLRANK Smart People are Scared of Artificial Intelligence
  5. 5. IPULLRANK.COM @ IPULLRANK Really Smart People…
  6. 6. It’s All Olivia Pope’s dad’s fault Either the Robots Enslave Us
  7. 7. Or Kill Us All
  8. 8. Or Evolve to a Point We Can’t Understand
  9. 9. Or we achieve singularity
  10. 10. IPULLRANK.COM @ IPULLRANK Singularity is Considered a Very Real Theory Ray Kurzweil believes that we will achieve singularity by 2045.
  11. 11. No matter what, it’s all Olivia Pope’sdad’sfault
  12. 12. IPULLRANK.COM @ IPULLRANK No Matter What Larry & Sergey are All Set Though
  13. 13. IPULLRANK.COM @ IPULLRANK Don’t Forget Isaac Asimov
  14. 14. IPULLRANK.COM @ IPULLRANK 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.
  15. 15. IPULLRANK.COM @ IPULLRANK But It Can Also Fuck It Up
  16. 16. IPULLRANK.COM @ IPULLRANK 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.
  17. 17. IPULLRANK.COM @ IPULLRANK Obama Had Some Measured Thoughts On His Way Out
  18. 18. 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.
  19. 19. IPULLRANK.COM @ IPULLRANK In the meantime though, it can get you a date (h/t @goutaste)
  20. 20. IPULLRANK.COM @ IPULLRANK And you can go there in a self-driving Uber Lyft
  21. 21. Machine Learning vs. Deep Learning vs. AI The Core Concepts
  22. 22. IPULLRANK.COM @ IPULLRANK They Are Not the Same Thing
  23. 23. IPULLRANK.COM @ IPULLRANK 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.
  24. 24. Artificial Intelligence as it is represented in sci-fi is “general” artificial intelligence. What we have achieved so far is “narrow” artificial intelligence.
  25. 25. IPULLRANK.COM @ IPULLRANK 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
  26. 26. IPULLRANK.COM @ IPULLRANK Experts Disagree on When General Intelligence Will Happen The primaryissue keeping this from happening is computingpower.
  27. 27. IPULLRANK.COM @ IPULLRANK Experts Disagree on When General Intelligence Will Happen The primaryissue keeping this from happening is computingpower.
  28. 28. IPULLRANK.COM @ IPULLRANK Accelerating Moore’s Law Google has been working on quantum computing to accelerate Moore’s Law
  29. 29. IPULLRANK.COM @ IPULLRANK 100mm times faster thana classicalcomputerby using a D-Wavequantumcomputer NewScientist.com
  30. 30. 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.”
  31. 31. IPULLRANK.COM @ IPULLRANK Supervised Learning The machine looks for patterns that match the labeled data that you provide and classifies new data based on that.
  32. 32. IPULLRANK.COM @ IPULLRANK Unsupervised Learning The machine identifies patterns in the data and creates clusters based on what it finds.
  33. 33. IPULLRANK.COM @ IPULLRANK Reinforcement Learning With reinforcement learning, the model is continually trained based on new data thereby improving the classifier’s ability to perform.
  34. 34. 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.”
  35. 35. That’s not what we’re talking about today.
  36. 36. 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.
  37. 37. IPULLRANK.COM @ IPULLRANK 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.
  38. 38. IPULLRANK.COM @ IPULLRANK 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.
  39. 39. IPULLRANK.COM @ IPULLRANK 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.
  40. 40. IPULLRANK.COM @ IPULLRANK 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.
  41. 41. Marketing Use Cases Subtitlegoes here.
  42. 42. IPULLRANK.COM @ IPULLRANK Predictive Analytics
  43. 43. IPULLRANK.COM @ IPULLRANK Marketing Campaign Performance Prediction
  44. 44. IPULLRANK.COM @ IPULLRANK Customer Churn Prediction
  45. 45. IPULLRANK.COM @ IPULLRANK Personalization
  46. 46. IPULLRANK.COM @ IPULLRANK Customer Segmentation
  47. 47. IPULLRANK.COM @ IPULLRANK Natural Language Processing
  48. 48. IPULLRANK.COM @ IPULLRANK Clustering & Classifying Keywords http://ipullrank.com/clustering-vs-classification-speed-keyword-research/
  49. 49. IPULLRANK.COM @ IPULLRANK Clustering & Classifying Keywords http://ipullrank.com/clustering-vs-classification-speed-keyword-research/
  50. 50. Follow Vicky Qian @vickyqian24
  51. 51. IPULLRANK.COM @ IPULLRANK Sentiment Analysis
  52. 52. IPULLRANK.COM @ IPULLRANK Natural Language Generation
  53. 53. IPULLRANK.COM @ IPULLRANK Computer Vision There are servicesthat leverage machine learningandcomputervisionto identifyobjects inpictures.
  54. 54. IPULLRANK.COM @ IPULLRANK Chatbots
  55. 55. IPULLRANK.COM @ IPULLRANK Training Chatbots Training chatbots is similar to training ML classifiers inthat you take a knowledge base andrun it throughNLPthentune it with regard to conversations.
  56. 56. Real World Examples Some things we workon
  57. 57. IPULLRANK.COM @ IPULLRANK We Re-Ranked the Inc. 500
  58. 58. IPULLRANK.COM @ IPULLRANK Follow Up Blog Content
  59. 59. IPULLRANK.COM @ IPULLRANK Company-level Report
  60. 60. IPULLRANK.COM @ IPULLRANK Retargeting Ads
  61. 61. IPULLRANK.COM @ IPULLRANK Super-specific Retargeting Ads
  62. 62. IPULLRANK.COM @ IPULLRANK We Built a Simple Marketing Automation System
  63. 63. IPULLRANK.COM @ IPULLRANK Each Contact Has a Unique URL
  64. 64. IPULLRANK.COM @ IPULLRANK Integrates with Reply
  65. 65. IPULLRANK.COM @ IPULLRANK LinkedIn Sales Navigator
  66. 66. IPULLRANK.COM @ IPULLRANK Prospectify for finding Emails Quickly
  67. 67. IPULLRANK.COM @ IPULLRANK Salesperson Writes Mail Merge Templates
  68. 68. IPULLRANK.COM @ IPULLRANK 42% Open Rate!
  69. 69. 732 Leads
  70. 70. IPULLRANK.COM @ IPULLRANK 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).
  71. 71. IPULLRANK.COM @ IPULLRANK 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
  72. 72. IPULLRANK.COM @ IPULLRANK Have You Met @tomcritchlowbot?
  73. 73. IPULLRANK.COM @ IPULLRANK The Bot wit the Solid Delivery.
  74. 74. IPULLRANK.COM @ IPULLRANK This is Twitter bot built from Markov Chains
  75. 75. IPULLRANK.COM @ IPULLRANK Adwords Scripts http://searchengineland.com/machine-learning-adwords-scripts-google-prediction-api-217936
  76. 76. IPULLRANK.COM @ IPULLRANK Programmatic Display Lookalike moderlign
  77. 77. Models Types of Models when you should Use Them
  78. 78. IPULLRANK.COM @ IPULLRANK There are Tons of Different Models Your best bet is to test and learn.
  79. 79. IPULLRANK.COM @ IPULLRANK Seriously Tonnnnnns
  80. 80. IPULLRANK.COM @ IPULLRANK The Uses of Each Type Are Difficult to Memorize
  81. 81. IPULLRANK.COM @ IPULLRANK Models & Use Cases Random Forest Lead Qualification Logistic Regression Customer Churn Prediction Decision Trees Customer Churn Prediction
  82. 82. IPULLRANK.COM @ IPULLRANK Models & Use Cases (Cont’d) Support Vector Machines Text Categorization Apriori Market Basket Analysis (Amazon) Naïve Bayes Sentiment Analysis RecommendationSystems Spam Classification
  83. 83. IPULLRANK.COM @ IPULLRANK K-Fold Cross Validation Try out a model and validate it using k-fold cross validation.
  84. 84. IPULLRANK.COM @ IPULLRANK How to Choose a Machine Learning Model https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice
  85. 85. Tools for Marketers Subtitlegoes here.
  86. 86. IPULLRANK.COM @ IPULLRANK
  87. 87. IPULLRANK.COM @ IPULLRANK
  88. 88. IPULLRANK.COM @ IPULLRANK 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.
  89. 89. IPULLRANK.COM @ IPULLRANK yHat Science Ops yHat allows you to deploy machinelearning modelsasREST APIsthat can then beintegrated with your site like any other API.
  90. 90. IPULLRANK.COM @ IPULLRANK Beeswax Bidder-as-a-Service Beeswax allows you to set up custom modelsto run your Display RTB campaigns.
  91. 91. Those are tools that allow marketers to take control with a data scientist.
  92. 92. IPULLRANK.COM @ IPULLRANK mTurk - Labeling Data for Supervised Learning ExploratoryData Analysis helps identifying general patterns in the data andserve as initial explorations of correlations.
  93. 93. IPULLRANK.COM @ IPULLRANK API.AI Generating Chatbots ExploratoryData Analysis helps identifying general patterns in the data andserve as initial explorations of correlations.
  94. 94. IPULLRANK.COM @ IPULLRANK NanoRep ExploratoryData Analysis helps identifying general patterns in the data andserve as initial explorations of correlations.
  95. 95. IPULLRANK.COM @ IPULLRANK MonkeyLearn & Orange We will primarilytalk aboutMonkeyLearn and Orange as two tools marketers can use to do machine learning right now.
  96. 96. IPULLRANK.COM @ IPULLRANK These Examples Use the Iris Petals Dataset https://archive.ics.uci.edu/ml/datasets/Iris
  97. 97. IPULLRANK.COM @ IPULLRANK Exploratory Data Analysis ExploratoryData Analysis helps identifying general patterns in the data andserve as initial explorations of correlations.
  98. 98. IPULLRANK.COM @ IPULLRANK Exploratory Data Analysis: Scatter Plot Two-dimensional scatterplot shows class density.
  99. 99. IPULLRANK.COM @ IPULLRANK Exploratory Data Analysis: Distributions Compare the distributions ofdifferent type ofiris.
  100. 100. IPULLRANK.COM @ IPULLRANK Classification Tree Observe the pattern across nodesto discoverimportant variables.
  101. 101. IPULLRANK.COM @ IPULLRANK Predictive Text Classification
  102. 102. IPULLRANK.COM @ IPULLRANK Import Text Mining Add-on Install the free text mining add-on in order to use Orange’s text mining capabilities.
  103. 103. IPULLRANK.COM @ IPULLRANK Load and Preprocess Dataset Preprocesstext to findmeaningful words only.
  104. 104. IPULLRANK.COM @ IPULLRANK Word Cloud Using the wordcloud, we candetermine the frequencyof keywords in the list.
  105. 105. IPULLRANK.COM @ IPULLRANK Hierarchical Clustering We can use this to determine similarityin the corpus or dataset.
  106. 106. IPULLRANK.COM @ IPULLRANK Hierarchical Clustering Once we understandthe hierarchy, we can digintothe documents in the viewerto see howthe model has organizedthem.
  107. 107. IPULLRANK.COM @ IPULLRANK Classification
  108. 108. IPULLRANK.COM @ IPULLRANK SVM: Linear vs. Non-linear LinearSVMoftenoutperforms non-linearin text classification.
  109. 109. IPULLRANK.COM @ IPULLRANK Confusion Matrix: Non-linear SVM Send misclassifiedsamples to corpus viewer.
  110. 110. IPULLRANK.COM @ IPULLRANK Nearest Neighbors
  111. 111. IPULLRANK.COM @ IPULLRANK Logistic Regression: Ridge vs. Lasso Logistic regression withl2 penaltyachieve higheraccuracy.
  112. 112. IPULLRANK.COM @ IPULLRANK Confusion Matrix: Lasso Send misclassifiedsamples to corpus viewer.
  113. 113. IPULLRANK.COM @ IPULLRANK Naive Bayes
  114. 114. IPULLRANK.COM @ IPULLRANK Compare Models LinearSVMandlogistic regression outperform the othertwomodels.
  115. 115. IPULLRANK.COM @ IPULLRANK Prediction Predict with winningclassifiers.
  116. 116. IPULLRANK.COM @ IPULLRANK Prediction SVMand logistic regressionall hit 100% accuracy.
  117. 117. IPULLRANK.COM @ IPULLRANKMonkeyLearn is a text miningcloudplatform.
  118. 118. IPULLRANK.COM @ IPULLRANKMonkeyLearn is a text miningcloudplatform. MonkeyLearn Now Works with Google Sheets
  119. 119. IPULLRANK.COM @ IPULLRANK Monkey Learn: Train Category Tree
  120. 120. IPULLRANK.COM @ IPULLRANK Monkey Learn: Tree Parameters
  121. 121. IPULLRANK.COM @ IPULLRANK Monkey Learn: Classify with Category Tree
  122. 122. IPULLRANK.COM @ IPULLRANK Codementor
  123. 123. IPULLRANK.COM @ IPULLRANK Experfy
  124. 124. IPULLRANK.COM @ IPULLRANK Kaggle
  125. 125. Wrapping Up Who am I and where am I from?
  126. 126. IPULLRANK.COM @ IPULLRANK I’M #ZORASDAD First and foremost.
  127. 127. IPULLRANK.COM @ IPULLRANK MY NAME IS MIKE KING Razorfish, Publicis Modem alum Full Stack Developer Full Stack Marketer Moz Associate
  128. 128. IPULLRANK.COM @ IPULLRANK I Run a Better Marketing Agency Called iPullRank
  129. 129. IPULLRANK.COM @ IPULLRANK We Do These Things Content Strategy SEO Paid Media Machine Learning Marketing Automation Measurement & Optimization
  130. 130. THAT’S ALL I’VE GOT
  131. 131. IPULLRANK http://ipullrank.com THANK YOU Michael King Managing Director (347) 391-4266 mike@ipullrank.com 02/21/2017

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