AC Notebooks overview

Document created by Makenna.Brei Advocate on Jun 1, 2017Last modified by Makenna.Brei Advocate on Jan 2, 2018
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Why use Analytic Notebooks?

 

  • Grow your impact by building repeatable analytic sequences that can be shared and re-used by others
  • Combine coding, documentation, visualization and interaction into a single linear narrative, with a clear record of each step in the process

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Access Powerful Libraries & Embrace Open Source Analytics

Python

R

SQL

Scala

Bash

Angular

Markdown

bokeh

ggplot2

googleVis

h2o

matplotlib

nltk

numpy

pandas

seaborn

scikit-learn

scipy

xgboost

Apache Spark ML

Apache Zeppelin

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Machine Learning

 

  • Apply leading Open Source tools for machine learning
  • Supervised learning
    • Gradient boosted trees
    • Random forests
    • Multilayer neural networks
    • Support vector machines
    • Generalized linear models
    • Smart hyper-parameter searches
  • Critical Capabilities
    • Natural Language processing
    • Partial dependency analysis
    • Model selection and evaluation
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