Training on Supervised Learning: Regression & Classification Models

Module 1: Introduction to Supervised Learning

• Overview of supervised vs. unsupervised learning

• Key concepts: labeled data, targets, features

• Regression vs. classification problems

• Introduction to Scikit-learn and Python tools for ML

Module 2: Data Preparation and Feature Engineering

• Exploratory Data Analysis (EDA) techniques

• Data cleaning, encoding categorical features

• Feature scaling and transformation

• Handling missing values and outliers

Module 3: Regression Models and Applications

• Simple and multiple linear regression

• Polynomial regression and feature interaction

• Use case: Forecasting sales using regression

• Business impact of regression modeling

Module 4: Model Evaluation for Regression

• Evaluation metrics: MAE, MSE, RMSE, R²

• Residual analysis and visualizations

• Cross-validation techniques

• Model optimization with grid and random search

Module 5: Classification Models and Applications

• Binary and multi-class classification

• Logistic regression, Decision Trees, K-NN

• Use case: Building classification models for customer churn

• Dealing with imbalanced datasets

Module 6: Advanced Classification Algorithms

• Support Vector Machines (SVM)

• Ensemble methods: Random Forest, Gradient Boosting

• ROC curves, Precision-Recall, AUC scoring

• Machine learning for business predictions case study

Module 7: Hyperparameter Tuning and Pipelines

• GridSearchCV and RandomizedSearchCV

• Building end-to-end Scikit-learn pipelines

• Feature selection techniques

• Regularization: Lasso, Ridge, ElasticNet

Module 8: Model Interpretation and Explainability

• Understanding feature importance

• SHAP and LIME for model interpretability

• Communicating model insights to non-technical audiences

• Ethical considerations in predictive modeling

Module 9: Model Deployment and Integration

• Saving and loading models with joblib

• Creating APIs for ML models (Flask or FastAPI)

• Introduction to deployment tools and cloud services

• Monitoring model performance post-deployment

Module 10: Final Project and Business Application

• Capstone project: Build a complete predictive pipeline

• Apply evaluating predictive model performance techniques

• Presenting outcomes to stakeholders

• Roadmap for implementing supervised ML in your organization