Machine learning principles

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Karolis Urbonas

Head of Machine Learning & Science, Amazon

Machine learning types

Machine learning - applying statistical or computer science methods on data to:

  1. Draw causal insights

    "What is causing our customers to cancel their subscription to our services?"

  2. Predict future events

    "Which customers are likely to cancel their subscription next month?"

  3. Understand patterns in data

    "Are there groups of customers who are similar and use our services in a similar way?"

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Supervised vs. unsupervised ML

Machine learning - applying statistical or computer science methods on data to:

  1. Draw causal insights

    SUPERVISED Machine Learning

  2. Predict future events

    SUPERVISED Machine Learning

  3. Understand patterns in data

    UNSUPERVISED Machine Learning

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Supervised ML data structure

inference-vs-prediction1

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Target variable

inference-vs-prediction2

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Input features

inference-vs-prediction

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Example input features

samplefraud

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Using input features

inference-vs-prediction4

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Predicting target variable

inference-vs-prediction5

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Unsupervised ML data structure

unsupervised1

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Unsupervised input features

input-feat-unsupervised

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Unsupervised ML results

clustering

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ML examples - Marketing

SUPERVISED Machine Learning:

  • Predict which customers are likely to purchase next month
  • Predict each customer's expected lifetime value

UNSUPERVISED Machine Learning:

  • Group customers into segments based on their past purchases
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ML examples - Finance

SUPERVISED Machine Learning:

  • Identify key transaction attributes that indicate a potential fraud
  • Predict which customers will default on their mortgage payments

UNSUPERVISED Machine Learning:

  • Group transactions into segments based on their attributes to understand which segments are the most profitable
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ML examples - Manufacturing

SUPERVISED Machine Learning:

  • Predict which items in production are likely faulty and should be manually inspected
  • Predict which machines are likely to break and need maintenance

UNSUPERVISED Machine Learning:

  • Group readings from machine sensors and identify anomalies for potential manufacturing malfunctions
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ML examples - Transportation

SUPERVISED Machine Learning:

  • Predict the expected delivery of the parcel
  • Identify the fastest route for driving
  • Predict product demand to prepare enough stock, rent/buy vehicles and hire workers
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Let's practice!

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