Congratulations!

Machine Learning for Marketing in Python

Karolis Urbonas

Head of Analytics & Science, Amazon

What have we learned?

  • Different types of machine learning - supervised, unsupervised, reinforcement
  • Machine learning steps
  • Data preparation techniques for different kinds of models
  • Predict telecom customer churn with logistic regression and decision trees
  • Calculate customer lifetime value
  • Predict next month transactions with linear regression
  • Measure model performance with multiple metrics
  • Segment customers based on their product purchase history with K-means and NMF
Machine Learning for Marketing in Python

What's next?

  • Dive deeper into each topic
  • Explore the datasets, change the parameters and try to improve model accuracy, or segmentation interpretability
  • Take on a project with other dataset, and build models with comments by yourself
  • Write a blog post with link to GitHub code once you finish your project
  • Test your knowledge in your job
Machine Learning for Marketing in Python

Thank you and great learning!

Machine Learning for Marketing in Python

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