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Module 3: Machine Learning for Classification — Machine Learning Zoomcamp

Lessons, materials, and key concepts for module 3 of Machine Learning Zoomcamp.

Machine Learning Zoomcamp > Module 3: Machine Learning for Classification

## 3. Machine Learning for Classification

• 3.1 Churn prediction project
• 3.2 Data preparation
• 3.3 Setting up the validation framework
• 3.4 EDA
• 3.5 Feature importance: Churn rate and risk ratio
• 3.6 Feature importance: Mutual information
• 3.7 Feature importance: Correlation
• 3.8 One-hot encoding
• 3.9 Logistic regression
• 3.10 Training logistic regression with Scikit-Learn
• 3.11 Model interpretation
• 3.12 Using the model
• 3.13 Summary
• 3.14 Explore more
• 3.15 Homework

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Lessons

Key concepts

• Trading Strategy
• Pandas
• Jupyter Notebooks
• scikit-learn
• Technical Indicators
• Linear Regression
• Logistic Regression
• Regularization

Homework and deadlines live in the course repository: machine-learning-zoomcamp.

Other modules in this course

• Module 1: Introduction to Machine Learning
• Module 2: Machine Learning for Regression
• Module 4: Evaluation Metrics for Classification
• Module 5: Deploying Machine Learning Models
• Module 6: Decision Trees and Ensemble Learning
• Module 8: Neural Networks and Deep Learning
• Module 9: Serverless Deep Learning
• Module 10: Kubernetes and TensorFlow Serving