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Intro to machine learning for web folks @ BlendWebMix

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Get a business understanding of ML by going through key concepts and concrete use cases that illustrate its possibilities for web-based companies.

In this presentation I introduce new technology that makes ML more accessible, and I explain in simple terms the limitations to what can be achieved. Finally, I discuss pragmatic considerations of real-world applications and I give a sneak peak at the Machine Learning Canvas — a framework for describing a predictive system that uses ML to provide value to its end user.

--

L'utilisation du Machine Learning s'est fortement développée ces dernières années, jusqu'à être présent aujourd'hui dans environ la moitié des applications que nous utilisons sur smartphone. Même s'ils n'ont pas connaissance du Machine Learning (ML), les utilisateurs d'applications mobile et web sont devenus demandeurs de fonctionnalités prédictives que le ML rend possibles. Par ailleurs, dans le cadre de l'entreprise, le ML représente un avantage compétitif important qui permet de valoriser ses data en les couplant à une intelligence machine.

Auparavant réservée aux grosses entreprises, cette technologie se démocratise grâce aux nouveaux outils de ML-as-a-Service et aux APIs de prediction. Afin d'en tirer profit, nous verrons ensemble les clés de compréhension du fonctionnement du machine learning, qui sous-tendent ses possibilités et ses limites. Nous verrons également comment amorcer son utilisation dans votre propre projet, au travers du Machine Learning Canvas qui permet de décrire un système où le ML est au cœur de la création de valeur.

Publié dans : Technologie
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  • DOWNLOAD THE BOOK INTO AVAILABLE FORMAT (New Update) ......................................................................................................................... ......................................................................................................................... Download Full PDF EBOOK here { https://urlzs.com/UABbn } ......................................................................................................................... Download Full EPUB Ebook here { https://urlzs.com/UABbn } ......................................................................................................................... Download Full doc Ebook here { https://urlzs.com/UABbn } ......................................................................................................................... Download PDF EBOOK here { https://urlzs.com/UABbn } ......................................................................................................................... Download EPUB Ebook here { https://urlzs.com/UABbn } ......................................................................................................................... Download doc Ebook here { https://urlzs.com/UABbn } ......................................................................................................................... ......................................................................................................................... ................................................................................................................................... eBook is an electronic version of a traditional print book THE can be read by using a personal computer or by using an eBook reader. (An eBook reader can be a software application for use on a computer such as Microsoft's free Reader application, or a book-sized computer THE is used solely as a reading device such as Nuvomedia's Rocket eBook.) Users can purchase an eBook on diskette or CD, but the most popular method of getting an eBook is to purchase a downloadable file of the eBook (or other reading material) from a Web site (such as Barnes and Noble) to be read from the user's computer or reading device. Generally, an eBook can be downloaded in five minutes or less ......................................................................................................................... .............. Browse by Genre Available eBOOK .............................................................................................................................. Art, Biography, Business, Chick Lit, Children's, Christian, Classics, Comics, Contemporary, CookBOOK, Manga, Memoir, Music, Mystery, Non Fiction, Paranormal, Philosophy, Poetry, Psychology, Religion, Romance, Science, Science Fiction, Self Help, Suspense, Spirituality, Sports, Thriller, Travel, Young Adult, Crime, EBOOK, Fantasy, Fiction, Graphic Novels, Historical Fiction, History, Horror, Humor And Comedy, ......................................................................................................................... ......................................................................................................................... .....BEST SELLER FOR EBOOK RECOMMEND............................................................. ......................................................................................................................... Blowout: Corrupted Democracy, Rogue State Russia, and the Richest, Most Destructive Industry on Earth,-- The Ride of a Lifetime: Lessons Learned from 15 Years as CEO of the Walt Disney Company,-- Call Sign Chaos: Learning to Lead,-- StrengthsFinder 2.0,-- Stillness Is the Key,-- She Said: Breaking the Sexual Harassment Story THE Helped Ignite a Movement,-- Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones,-- Everything Is Figureoutable,-- What It Takes: Lessons in the Pursuit of Excellence,-- Rich Dad Poor Dad: What the Rich Teach Their Kids About Money THE the Poor and Middle Class Do Not!,-- The Total Money Makeover: Classic Edition: A Proven Plan for Financial Fitness,-- Shut Up and Listen!: Hard Business Truths THE Will Help You Succeed, ......................................................................................................................... .........................................................................................................................
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Intro to machine learning for web folks @ BlendWebMix

  1. 1. Intro to Machine Learning
 for web folks “Machine Learning, je m’y mets dès demain” @louisdorard #blendwebmix 2015
  2. 2. –Mike Gualtieri, Principal Analyst at Forrester “Predictive apps are the next big thing in app development.”
  3. 3. Lars Trieloff @trieloff (see source)
  4. 4. –Waqar Hasan, VISA “Predictive is the ‘killer app’ for big data.”
  5. 5. Amazon for David Jones (@d_jones, see source)
  6. 6. Amazon for David Jones (@d_jones, see source)
  7. 7. 1. Machine Learning 2. Data
  8. 8. TECH ??
  9. 9. –Charles Parker, PhD, Allston Trading “ML isn’t about ML”
  10. 10. BIZ DESIGN RECH code code code
  11. 11. BLEND !!
  12. 12. @louisdorard
  13. 13. “Where makers of Predictive APIs and apps meet”
  14. 14. Machine Learning Use cases Limitations Modern tools Case study ML Canvas
  15. 15. Demystifying
 Machine Learning
  16. 16. “Which type of email is this? — Spam/Ham” 

  17. 17. “Which type of email is this? — Spam/Ham” 
 Classification
  18. 18. I O “Which type of email is this? — Spam/Ham” 

  19. 19. ??
  20. 20. “How much is this house worth? — X $” 
 -> Regression
  21. 21. Bedrooms Bathrooms Surface (foot²) Year built Type Price ($) 3 1 860 1950 house 565,000 3 1 1012 1951 house 2 1.5 968 1976 townhouse 447,000 4 1315 1950 house 648,000 3 2 1599 1964 house 3 2 987 1951 townhouse 790,000 1 1 530 2007 condo 122,000 4 2 1574 1964 house 835,000 4 2001 house 855,000 3 2.5 1472 2005 house 4 3.5 1714 2005 townhouse 2 2 1113 1999 condo 1 769 1999 condo 315,000
  22. 22. Bedrooms Bathrooms Surface (foot²) Year built Type Price ($) 3 1 860 1950 house 565,000 3 1 1012 1951 house 2 1.5 968 1976 townhouse 447,000 4 1315 1950 house 648,000 3 2 1599 1964 house 3 2 987 1951 townhouse 790,000 1 1 530 2007 condo 122,000 4 2 1574 1964 house 835,000 4 2001 house 855,000 3 2.5 1472 2005 house 4 3.5 1714 2005 townhouse 2 2 1113 1999 condo 1 769 1999 condo 315,000
  23. 23. ML is a set of AI techniques where “intelligence” is built by referring to examples
  24. 24. Use cases
  25. 25. • Real-estate • Spam • Priority inbox • Crowd prediction property price email spam indicator email importance indicator location & context #people Zillow Gmail Gmail Tranquilien
  26. 26. I. Get more customers • Reduce churn • Score leads • Optimize campaigns customer churn indicator customer revenue customer & campaign interest indicator
  27. 27. II. Serve customers better • Cross-sell • Increase engagement • Optimize pricing customer & product purchase indicator user & item interest indicator product & price #sales
  28. 28. III. Serve customers more efficiently • Predict demand • Automate tasks • Use predictive enterprise apps context demand credit application repayment indicator
  29. 29. Predictive enterprise apps • Priority filtering • Message routing • Auto-configuration message priority indicator request employee user & actions settings RULES
  30. 30. –Katherine Barr, Partner at VC-firm MDV "Pairing human workers with machine learning and automation will transform knowledge work and unleash new levels of human productivity and creativity."
  31. 31. Limitations
  32. 32. Need examples of inputs AND outputs
  33. 33. What if not enough data points?
  34. 34. What if similar inputs have dissimilar outputs?
  35. 35. Bedrooms Bathrooms Price ($) 3 2 500,000 3 2 800,000 1 1 300,000 1 1 800,000
  36. 36. Bedrooms Bathrooms Surface (foot²) Year built Price ($) 3 2 800 1950 500,000 3 2 1000 1950 800,000 1 1 500 1950 300,000 1 1 500 2014 800,000
  37. 37. • Need examples of inputs AND outputs • Need enough examples • Need enough“features”
  38. 38. –@louisdorard “A model can only be as good as the data it was given to train on”
  39. 39. –McKinsey & Co. (2011) “A significant constraint on realizing value from big data will be a shortage of talent, particularly of people with deep expertise in statistics and machine learning.”
  40. 40. MLaaS & Predictive APIs:
 ML for all
  41. 41. HTML / CSS / JavaScript
  42. 42. HTML / CSS / JavaScript
  43. 43. squarespace.com
  44. 44. The two phases of machine learning: • TRAIN a model • PREDICT with a model
  45. 45. The two methods of predictive APIs: • TRAIN a model • PREDICT with a model
  46. 46. The two methods of predictive APIs: • model = create_model(dataset) • predicted_output = create_prediction(model, new_input)
  47. 47. The two methods of predictive APIs: • model = create_model(‘training.csv’) • predicted_output = create_prediction(model, new_input)
  48. 48. From Large to Small & Medium Enterprises • recommendations in e-commerce • => 71% increase in revenue • churn detection • => 11% increase in retention
  49. 49. ChurnSpotter.io
  50. 50. Microsoft Azure ML
  51. 51. PredictionIO
  52. 52. Case study:
 churn analysis
  53. 53. • Who: SaaS company selling monthly subscription • Question asked:“Is this customer going to leave within 1 month?” • Input: customer • Output: no-churn or churn • Data collection: history up until 1 month ago • Baseline: if no usage for more than 15 days then churn
  54. 54. Learning: OK but • How to represent customers? • What to do after predicting churn?
  55. 55. Customer representation: • basic info (age, income, etc.) • usage of service (# times used app, avg time spent, features used, etc.) • interactions with customer support (how many, topics of questions, satisfaction ratings)
  56. 56. Taking action to prevent churn: • contact customers (in which order?) • switch to different plan • give special offer • no action?
  57. 57. Measuring accuracy: • #TP (we predict customer churns and he does) • #FP (we predict customer churns but he doesn’t) • #FN (we predict customer doesn’t churn but he does) • Compare to heuristic/baseline
  58. 58. Return On Investment: • Taking action for each TP (and FP) has a cost • For each TP we“gain”:
 (success rate of action) * (revenue /cust. /month) • Imagine… • perfect predictions • revenue /cust. /month = 10€ • success rate of action = 20% • cost of action = 2€ • Which ROI?
  59. 59. Machine Learning Canvas
  60. 60. PREDICTIONS OBJECTIVES DATA Context Who will use the predictive system / who will be affected by it? Provide some background. Value Proposition What are we trying to do? E.g. spend less time on X, increase Y... Data Sources Where do/can we get data from? (internal database, 3rd party API, etc.) Problem Question to predict answers to (in plain English) Input (i.e. question "parameter") Possible outputs (i.e. "answers") Type of problem (e.g. classification, regression, recommendation...) Baseline What is an alternative way of making predictions (e.g. manual rules based on feature values)? Performance evaluation Domain-specific / bottom-line metrics for monitoring performance in production Prediction accuracy metrics (e.g. MSE if regression; % accuracy, #FP for classification) Offline performance evaluation method (e.g. cross-validation or simple training/test split) Dataset How do we collect data (inputs and outputs)? How many data points? Features Used to represent inputs and extracted from data sources above. Group by types and mention key features if too many to list all. Using predictions When do we make predictions and how many? What is the time constraint for making those predictions? How do we use predictions and confidence values? Learning predictive models When do we create/update models? With which data / how much? What is the time constraint for creating a model? Criteria for deploying model (e.g. minimum performance value — absolute, relative to baseline or to previous model) IDEASPECSDEPLOYMENT
  61. 61. BACKGROUND ENGINE SPECS INTEGRATION
  62. 62. PREDICTIONS OBJECTIVES DATA BACKGROUND ENGINE SPECS INTEGRATION
  63. 63. PREDICTIONS OBJECTIVES DATA BACKGROUND End-user Value prop Sources ENGINE SPECS ML problem Perf eval Preparation INTEGRATION Using pred Learning modelINTEGRATION Using pred Learning model
  64. 64. Why fill in ML canvas? • Target the right problem for your company • Choose right algorithm, infrastructure, or ML solution • Guide project management • Improve team communication
  65. 65. machinelearningcanvas.com
  66. 66. Recap
  67. 67. • ML to create value from data • 2 phases: TRAIN and PREDICT • MLaaS & Predictive APIs make it more accessible • Good data is essential • What do we do with predictions? • Accuracy is not the objective! A/B test? • Start with the ML Canvas • Later: deploy, maintain, improve…
  68. 68. @louisdorard louisdorard.com

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