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Data Science Salon Miami Presentation

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Presentation delivered at Data Science Salon in Miami on February 8, 2018.

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Data Science Salon Miami Presentation

  1. 1. Introduction to Machine Learning - Marketing Use Case Greg Werner / 2/8/2018
  2. 2. 2 Cup of Data is Hiring Data Scientists! Atlanta, GA We’re hiring!
  3. 3. 3 Basic Agenda 01 Goals 02 Data Science Process 03 Machine Learning Primer 04 Optimization Techniques 05 Marketing Examples
  4. 4. 4 Timeline Agenda 1Understand the Data Science Process 2What Algorithms to apply and when 3Basic differences between ML and DL 4Some examples
  5. 5. Hacking Skills Programming, data munging Domain Level Expertise The best data scientists are those that understand the problems they are try 5 The Data Science Persona Math and Stats Mathematical skills, mostly involved with statistics, algebra, and some calc. The Data Scientist The ideal data scientist has skills from all three domains!
  6. 6. Fetch Fetch your data from single or disparate sources. Clean Clean your data to prepare it for analysis. For example, eliminate null values, add missing data. Prepare Data selection, preprocessing, and transformations. Visualizations help, too. Deploy and Monitor Operationalize your Model and monitor. Don’t be afraid to challenge your models. Evaluate Select the best performing model. Establish a common performance metric! Train Model Train your model based on supervised, semi supervised, or unsupervised learning techniques. 6 Data Science Workflow Process
  7. 7. 7 Exploratory Data Analysis (EDA) Prepare the Data Spot Check Algorithms Improve Results Tell the Story
  8. 8. 8 Data Preparation Primer
  9. 9. 9 Data Preparation: Selection
  10. 10. 10 Data Preparation: Preprocess
  11. 11. 11 Data Preparation: Transform
  12. 12. 12 Spot Check Algorithms
  13. 13. 13 Grouping Algorithms
  14. 14. 14 Spot Check Algorithms
  15. 15. 15 Grouping Algorithms by Similarity
  16. 16. 16 Deep Learning Why Deep Learning? Slide by Andrew Ng, all rights reserved.
  17. 17. 17 “When you hear the term deep learning, just think of a large deep neural net. Deep refers to the number of layers typically and so this kind of the popular term that’s been adopted in the press. I think of them as deep neural networks generally.” Jeff Dean
  18. 18. 18 Some Deep Learning Innovations ... Automatic feature extraction from raw data, also called feature learning.
  19. 19. 19 Deep Learning (cont.) Source: KDNuggets.com all rights reserved.
  20. 20. 20 Deep Learning (cont.) 1. Input a set of training examples 2. For each training example xx, set corresponding input activation and: a. Feedforward b. Output error c. Backpropagate the error 3. Gradient descent
  21. 21. 21 Linear Components with Icons Key Takeaways You can’t get around the data munging, for now, anyway. Deep Learning is used mostly for supervised learning problems Automating the ML and DL pipelines are important Data science is a team effort A.I. doesn’t exist yet. But it’s less of a mouth full.
  22. 22. 22 Some Demos
  23. 23. Thank You!! 23 greg@cupofdata.com linkedin.com/in/wernergreg/ www.cupofdata.com 3423 Piedmont Rd NE Atlanta, GA 30305

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