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Katerina Bosko has completed

Machine Learning for Marketing in Python

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4 hours
4,450 XP
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Course Description

The rise of machine learning (almost sounds like "rise of the machines"?) and applications of statistical methods to marketing have changed the field forever. Machine learning is being used to optimize customer journeys which maximize their satisfaction and lifetime value. This course will give you the foundational tools which you can immediately apply to improve your company’s marketing strategy. You will learn how to use different techniques to predict customer churn and interpret its drivers, measure, and forecast customer lifetime value, and finally, build customer segments based on their product purchase patterns. You will use customer data from a telecom company to predict churn, construct a recency-frequency-monetary dataset from an online retailer for customer lifetime value prediction, and build customer segments from product purchase data from a grocery shop.
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  1. 1

    Machine learning for marketing basics


    In this chapter, you will explore the basics of machine learning methods used in marketing. You will learn about different types of machine learning, data preparation steps, and will run several end to end models to understand their power.

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    Why use ML for marketing? Strategies and use cases
    50 xp
    Identify supervised learning examples
    50 xp
    Supervised vs. unsupervised learning
    100 xp
    Preparation for modeling
    50 xp
    Investigate the data
    100 xp
    Separate numerical and categorical columns
    100 xp
    Encode categorical and scale numerical variables
    100 xp
    ML modeling steps
    50 xp
    Split data to training and testing
    100 xp
    Fit a decision tree
    100 xp
    Predict churn with decision tree
    100 xp

In the following tracks

Marketing Analytics


Collaborator's avatar
Adel Nehme
Karolis Urbonas HeadshotKarolis Urbonas

Head of Machine Learning and Science

Karolis is currently leading a Machine Learning and Science team at Amazon Web Services. He's a data science enthusiast obsessed with machine learning, analytics, neural networks, data cleaning, feature engineering, and every engineering puzzle he can get his hands on.
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