This talk was delivered in Chennai on September 14th, 2019.

1. Custom Machine Learning Recipes for the Enterprise
Sudalai Rajkumar @ SRK
14th September, 2019
Chennai

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H2O.ai Product Suite
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H2O Package
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Data Science Workflow
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Data
Procurement
Dataset
Preparation
Feature
Engineering
Predictions,
Deployment
Model
Training

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Driverless AI
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Automatic Machine Learning platform that automates complex Data Science tasks

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Driverless AI - Workflow
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Machine Learning Interpretation
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https://www.h2o.ai/oreilly-mli-booklet-2019/

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Machine Learning Interpretation
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Goal:
Get a general understanding of how
the model works
Example:
What were the most important
features to predict default?
Techniques:
● Variable Importance
● Partial Dependency Plots
● Surrogate simple decision tree
Global
Goal:
Understand why a particular record
received that prediction
Example:
Why did the model predict such a
high score for this customer
Techniques:
● K-LIME
● Leave One Covariate Out
(LOCO)
Local

9. Demo

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Driverless AI - NLP
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0

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Custom Machine Learning Recipes
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Bring Your Own Recipes
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https://github.com/h2oai/driverlessai-recipes

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BYOR - Text Preprocessing
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https://github.com/h2oai/driverlessai-recipes/blob/master/transformers/nlp/text_preprocessing_transformer.py
● Lower casing
● Removal of Punctuations
● Removal of Stopwords
● Removal of Frequent words
● Removal of Rare words
● Stemming
● Lemmatization
● Removal of emojis
● Removal of emoticons
● Conversion of emoticons to words
● Conversion of emojis to words
● Removal of URLs
● Removal of HTML tags
● Chat words conversion
● Spelling correction
https://www.kaggle.com/sudalairajkumar/getting-started-with-text-preprocessing

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BYOR - Text Meta Features
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https://github.com/h2oai/driverlessai-recipes/blob/master/transformers/nlp/text_meta_transformers.py
● Number of words
● Number of characters
● Number of unique words
● Number of uppercase words
● Number of numerics
● Number of punctuations
● Mean word length

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BYOR - Text Readability Features
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https://github.com/h2oai/driverlessai-recipes/blob/master/transformers/nlp/text_readability_transformers.py
● Syllable count
● Poly-syllable count
● Average syllables per word
● Smog index
● Flesch reading ease score
https://github.com/shivam5992/textstat

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BYOR - Text Features
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● Text Sentiment
○ Get the sentiment scores from pretrained models / packages
● Language Detection
○ To detect the language of the given text
● POS Tagging
○ Count of nouns, verbs, adjectives etc
● Topic Modeling
○ To do topic modeling and use the topics as features
● Text Summarization
○ Summarize the text and use that as new feature

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BYOR - Text Similarity Features
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● N-gram similarity
○ Count of common n-grams
○ Jaccard similarity
○ Dice similarity
○ Edit distance similarity
● Fuzzy Similarity
○ Partial ratio
○ Token set ratio
○ Token sort ratio
● Embedding similarity
○ Glove
○ Fasttext
○ BERT

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Take a Test Drive!
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We are hiring!
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Data Scientist / Engineers in Customer Solutions / Support team
● Experience in data science / engineering field with strong basics
● Exposure to big data technologies (spark/hadoop), cloud/docker etc
● Strong in one or more Python / R / Java / Scala and ofcourse Linux (shell scripting)
● Hands on, highly motivated and can keep up with our startup pace (ie a fast learner)
Please mail to:
sairaam.varadarajan@h2o.ai
sudalai.rajkumar@h2o.ai

20. Thank You!
@sudalairajkumar
www.linkedin.com/in/sudalairajkumar/
www.kaggle.com/sudalairajkumar