Practical Machine Learning Fellowship

Summary

Build useful machine learning models that deliver data-driven insights to help your company make better decisions that can improve revenue, reduce costs, create new opportunities, identify new ideas, improve the customer experience and more.

Associated project work

Students will develop a series of models to solve both Regression and Classification problems. The exercises give students practice using essential Scikit-Learn tools (including transformers and pipelines) and emphasize concepts like hyperparameter optimization and feature importance.

Students will develop a series of models to predict a venue’s star rating from various features. Working from 100MB of real-world data, they will start with location-based models before building models based on other attributes of the venues. Finally, an ensemble model will blend the individual models into a final prediction of the venue’s popularity.

Introduction to Machine Learning

Machine learning basics Types of machine learning Training a supervised ML model Choosing a family of models Defining a cost function Finding the optimal model Conclusion

Scikit-learn API

Classes vs. objects Predictors Transformers Pipelines Conclusion

Regression

Linear regression Linear regression with a single feature Regression metrics Linear regression with multiple features Optimization Adding features Regularization Conclusion

Bias, Variance, and Overfitting

Train a decision tree model In-sample error Out-of-sample error Bias-variance tradeoff Cross-validation strategies Grid search for tuning hyperparameters Test data set: evaluating the final model Conclusion

Introduction to Classification

Customer classification Logistic regression Classification metrics: accuracy Classification metrics: precision and recall Other classification metrics Probabilistic models Multiclass classification problems Conclusion

Classification: Predicting Customer Churn

Customer data Encoding Categorical Variables One-hot encoding of all categorical columns Training a classifier Improving the model: random forest classifier Model comparison Dealing with unbalanced classes Conclusion

Clustering

K-Means clustering Metrics for clustering Choosing the number of clusters Clustering on customers data Conclusion

Dimensionality Reduction

Principal component analysis (PCA) Using real-world data PCA in practice - scaling the data Principal axes and explained variance Choosing number of components to use Interpreting the components Conclusion Appendix: PCA in detail

Scikit-learn Workflow

Remark Categorical features Combining features using ColumnTransformer Building custom scikit-learn classes Building a custom transformer using LATITUDE and LONGITUDE Using FunctionTransformer to build stateless transformers Feature unions Imputation A fancy display... Conclusion

Prerequisites

Basic Python

Basic linear algebra

Basic statistics

The Data Incubator

Practical Machine Learning Fellowship

Summary

Associated project work