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Alexandra Johnson - Software Engineer, SigOpt
alexandra@sigopt.com
Twitter: @alexandraj777
Machine Learning Fundamentals

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What Is Machine Learning?

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
(Don't say "machine" or "learning")
What Is Machine Learning?

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
(Don't say "machine" or "learning")
A solution to a problem that improves with data
What Is Machine Learning?

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
(Don't say "machine" or "learning")
A solution to a problem that improves with data
Data: emails, articles, images, list of homes
What Is Machine Learning?

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
(Don't say "machine" or "learning")
A solution to a problem that improves with data
Data: emails, articles, images, list of homes
Problem: label an email as spam (classification), predict a
home's price (regression), and others
What Is Machine Learning?

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● Problem: quickly identify if an email is spam or not spam
● Data: a list of emails, a list of "labels" spam or not spam
● Goal: function that will correctly label never-before-seen
emails as spam or not spam
Example: Classify Spam Emails

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● Pick a model: xgboost, random forest, mxnet CNN, etc
● Transform your data to be readable by the model
● Feature engineering: explore your data to pick out
information you is important
Build - Train - Tune - Deploy

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● Model: random forest
● Features: percentage of misspelled words, number of
words from a blacklist, domain name of email sender
Build - Train - Tune - Deploy Example
def extract_features(email):
return [
email.mispelled_words,
email.words_on_blacklist,
email.sender.domain,
]

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● Expose the model to your data so it can better solve your
problem
● Think of a model as a class, this method has already
been implemented
● Compute intensive, best done on a server
Build - Train - Tune - Deploy

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● Model: random forest
● Features: percentage
of misspelled words,
number of words from
a blacklist, domain
name of email sender
Build - Train - Tune - Deploy Example
email_features = [
[0.1, 1, 'hotmail.com'],
[0.7, 20, 'gmail.com'],
[0.3, 92, 'yahoo.com'],
]
labels = [0, 1, 1]
model = RandomForest()
model.train(email_features, labels)

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Build - Train - Tune - Deploy
● Models have tunable knobs, aka "hyperparameters"
● Different hyperparameters = different performance
● Train data set for training, validation data set for
measuring performance
● Overfitting: your model is really good on your old data, but
really bad on never-before-seen data

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Build - Train - Tune - Deploy Example
def evaluate(num_leaves, max_depth):
train_data, train_labels, validation_data, validation_labels
= split(email_features, labels)
model = RandomForest(num_leaves=num_leaves, max_depth=max_depth)
model.train(train_data, train_labels)
validation_score = model.score(validation_data, validation_labels)
return validation_score

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Build - Train - Tune - Deploy
● We train our model to solve our problem on old data but
we really want to solve our problem on new data
● Create a REST endpoint for accessing the model
● A/B test different versions

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model = RandomForest(best_hyperparameters)
model.train(emails, labels)
def is_spam(email):
email_features = extract_features(email)
return model.predict(email_features)
Build - Train - Tune - Deploy Example

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Thanks! Questions?
alexandra@sigopt.com
Twitter: @alexandraj777