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Artificial Intelligence in Action

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Third episode ! let's learn more about the AI revolution and create your own machine learning model in Azure ML Studio !

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Artificial Intelligence in Action

  1. 1. @TheBenimou
  2. 2. 4 From ML to AI What you will get from this live session Key Concepts Let’s practice !
  3. 3. Machine Learning everywhere Why is machine learning exciting ? Self driving cars Voice recognition Alphago
  4. 4. Speaker : Benjamin Ejzenberg @TheBenimou
  5. 5. Speaker : Benjamin Ejzenberg @TheBenimou Benjamin Ejzenberg https://thebenimou.github.io/
  6. 6. Episode 1 Click here
  7. 7. Episode 2 Click here
  8. 8. Artificial Intelligence Machine Learning Deep Learning 1960 1970 1980 1990 2000 2010 2020
  9. 9. Factors Driving the Artificial Intelligence Revolution
  10. 10. Data Deluge Moore’s Law Neural networks
  11. 11. Healthcare companies are taking Amazon very seriously Markets Insider - nov. 2017 Microsoft Wants to Use AI and Machine Learning to Discover a Cure for Cancer Futurism - nov. 2017 Google's new project will gather health data from 10000 people New Scientist - avr. 2017 How Tech Giants Are Investing In Healthcare
  12. 12. Types of Machine learning
  13. 13. Who bought my last product ?
  14. 14. Feature A : Wages Feature B : Age
  15. 15. Feature A : Wages Feature B : Age Did they buy my last product ? Yes No
  16. 16. Feature A : Wages Feature B : Age Did they buy my last product ? Yes No
  17. 17. Feature A : Wages Feature B : Age Did they buy my last product ? Yes No boundary
  18. 18. Feature A : Wages Feature B : Age Did they buy my last product ? Yes No
  19. 19. Feature A : Wages Feature B : Age So I can guess what to do with new customers
  20. 20. Data is labelled with a class or a value We have examples of inputs and outputs Goal : predict a class or value
  21. 21. Data is labelled with a class or a value We have examples of inputs and outputs Goal : predict a class or value classification regression
  22. 22. data is not labelled Feature A Feature B
  23. 23. Feature A Feature B Goal : determine data groupings / patterns
  24. 24. Feature A Feature B Goal : determine data groupings / patterns
  25. 25. Feature A Feature B Goal : determine data groupings / patterns
  26. 26. Feature A Feature B So I can guess the category for my new customers
  27. 27. Data is not labelled Goal : determine data groupings / patterns Self-guided learning algorigthm
  28. 28. So what do you want to do ? Predict Values Find unusual occurences Discover Structure Predict Between 2 categories Predict Between categories
  29. 29. Predict Values Forecast the future by estimating the relationship between variables. Estimate product demand Predict sales figures
  30. 30. Find unusual occurences Identify and predict rare or unusual data points. Predict Credit risk Detect fraud Predict network intrusions
  31. 31. Discover Structure Separate similar data points into intuitive groups. customer segmentation Predict customer tastes Determine which products fail the same way
  32. 32. Predict Between 2 categories Separate similar data points into intuitive groups. Is this tweet positive? A/B Will this customer renew their service? Which of two coupons draws more customers?
  33. 33. Predict Between categories Forecast the future by estimating the relationship between variables. What is the mood of this tweet? A/B/C Which service will this customer choose? Which of several promotions draws more customers?
  34. 34. Let’s practice
  35. 35. Your Client Your client is a Car Dealer ML Problem : Pricing Strategy Your client wants you to build a model to estimate the price at which to sell the products. Overview of the data you will use You have access to all the cars that have been sold by the dealer. For each sale, you have all the characteristics of the vehicle as well as the final sale price What algorithm will you use ? Classification / Regression ? For the experiment , we will use Azure Machine Learning Studio : https://studio.azureml.net/ Your Client
  36. 36. What algorithm will you use ? Better Questions, Better Answers Classification / Regression ?
  37. 37. Step 1 : Data Import Use “Automobile price data” Visualize the dataset Step 2 : Data Preparation Do some preprocessing Clean missing Data Step 3 : Features Selection In the dataset, each column is a feature (price is the target) Find a good set of features for creating a predictive model Step 4 : Model Selection and training Split the data between : keep 75% to train and 25% to test Use a linear regression algorithm to predict the price Train the model on 75% of the dataset Step 5 : Model Evaluation Test the model on the remaining 25% the dataset 1 2 2 3 44 4 5 5 Overview of our Data Science Project Workflow
  38. 38. Improve the model Try to change the features Try to change the parameters of the model used Try to use other regression models Always check the model evaluation to assess the performance of your new model Deploy your model Deploy your model as a “webservice” You can call the API at anytime with a new car for which you want to estimate/predict the price 1 2 2 3 44 4 5 5 Overview of our Data Science Project Workflow

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