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Data Science Salon Miami Presentation
This document provides an introduction and agenda for a machine learning marketing use case presentation. It begins with introducing the presenter and their company Cup of Data, which is hiring data scientists. The basic agenda is then outlined, covering goals, the data science process, a machine learning primer, optimization techniques, and marketing examples. The remainder of the document dives deeper into each section of the agenda, providing overviews and explanations of topics like the data science workflow process, data preparation techniques, grouping algorithms, and deep learning.
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Data Science Salon Miami Presentation
- 1. Introduction to Machine Learning- Marketing Use Case Greg Werner / 2/8/2018
- 2. 2 Cup of Datais Hiring Data Scientists! Atlanta, GA We’re hiring!
- 3. 3 Basic Agenda 01 Goals 02Data Science Process 03 Machine Learning Primer 04 Optimization Techniques 05 Marketing Examples
- 4. 4 Timeline Agenda 1Understand the Data Science Process 2What Algorithmsto apply and when 3Basic differences between ML and DL 4Some examples
- 5. Hacking Skills Programming, data munging DomainLevel 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. Fetch Fetch your datafrom 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 Exploratory Data Analysis(EDA) Prepare the Data Spot Check Algorithms Improve Results Tell the Story
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- 15. 15 Grouping Algorithms bySimilarity
- 16. 16 Deep Learning Why DeepLearning? Slide by Andrew Ng, all rights reserved.
- 17. 17 “When you hearthe 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 Some Deep LearningInnovations ... Automatic feature extraction from raw data, also called feature learning.
- 19. 19 Deep Learning (cont.) Source:KDNuggets.com all rights reserved.
- 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 Linear Components withIcons 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.
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