The document provides a foundational overview of machine learning, defining it as a problem-solving approach that improves through data. It outlines the steps involved in building a machine learning model, including data transformation, feature engineering, model training, tuning, and deployment, using spam email classification as an example. Key concepts such as model selection, hyperparameters, overfitting, and creating endpoints for model access are also discussed.

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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?

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(Don't say "machine" or "learning")
What Is Machine Learning?

4. © 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?

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(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?

6. © 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