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Intro to ML for product school meetup
- 1. Intro to Machine Learning For Product Managers
- 2. About me Build Things Product Give & Tech
- 3. What’s the sexist job you can think of?
- 4. Data Scientist: The Sexiest Job of the 21st Century “ ”Harvard Business Review
- 5. What are we going to talk about? Intro to ML Evaluation metrics Can we explain it? Algorithms, pipelines, data cleaning, bias...
- 6. Intro to Machine Learning
- 7. Intro to Machine Learning
- 8. Intro to Machine Learning
- 9. Intro to Machine Learning Supervised Learning Regression Prediction of Numerical values Classification Prediction of categorical values Time series Regression/Classification with Time Axis Importance Churn: Yes/No Customer Segmentation Anomaly Detection Stock Prices Predictive Maintenance Demand Prediction Churn in next 2 weeks Heart Rate/Blood Pressure Risk/Credit score LTV
- 10. Model Evaluation Metrics WWMCD? Over time How to measure ML models?
- 11. Intro to Machine Learning - Target Metrics Supervised Learning Regression Prediction of Numerical values Classification Prediction of categorical values ● MAE ● RMSE ● R1, R2 ● Normalized MAE/RMSE ● MSPE ● RMSPE ● ... ● Recall ● Precision ● Accuracy ● F1, F2 ● AUC ● Cost Metric ● ...
- 12. Intro to Machine Learning - Evaluation Metrics
- 13. What would Marty Cagan do?
- 14. How to measure ML models? Over time - Drift
- 15. Intro to Machine Learning - Explainability
- 16. Intro to Machine Learning - Explainability How does the model make decisions?
- 17. Global Explainability What are the important features? What happens if we change their value (PDP)?
- 18. How can we explain a local prediction?
- 19. Local Explainability What if - ICE
- 20. Local Explainability Local feature sensitivity (Lime/SHAP)
- 21. Local Explainability K- Nearest Neighbors
- 22. If you’re reading this We Made it :)
- 23. How to be awesome Listen and don’t be an a-hole Business value comes first If you can solve without ML, go for it
- 24. Intro to Machine Learning - Terms you got to know Term Explanation Feature Engineering Feature engineering is a key step in applying ML. A “feature” is an attribute shared by all the data units used by the ML model. Finding the right features is key to creating an effective ML model, but it’s also difficult and time-consuming. Learn more about feature engineering here. SKLearn and TensorFlow SKLearn - also called SciKit Learn - and TensorFlow are free, open-source software libraries for ML and deep learning workflows, models, and algorithms. SKLearn and TensorFlow also work together as SciKit Flow, a simplified interface to help people get started on predictive analytics and ML. Learn more about SKLearn and TensorFlow here. Hyperparameters Optimization Hyperparameters are the rules that govern training algorithms. The success of a training model - and thus the entire ML model - depends on optimizing the hyperparameters to the correct settings. Learn more about hyperparameter optimization here. Data splitting Before data analysts can use data for machine learning models, they need to split the data. Usually, data is split into three datasets: a training dataset, a validation dataset, and a test dataset. Learn more about datasets and data splitting here. Leakage and Overfitting Overfitting is when an ML model produces training results that are too accurate, so much so that it can’t produce reliable results with real world data. Leakage means that unexpected information leaked in to the training dataset, causing unrealistic results. Leakage has a number of different causes, and it is itself one of many causes of overfitting. Learn more about leakage and overfitting here and here.
- 25. Feedback me :) eBook: How to add AI/ML to your product
Editor's Notes
- Individual Conditional Expectation