Contents
DAY 1 – Fundamentals and First Operational Models
Module 1: From Classical Techniques to ML
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Limits of traditional expert systems
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The three Machine Learning paradigms: Supervised, Unsupervised, Reinforcement
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Practical application: Markov Chain for text generation
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From determinism to probabilistic predictions
Module 2: Linear Regression
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Numerical value prediction: from y = ax + b to the multidimensional world
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Standard ML workflow: Problem Definition → Deployment
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Practical Project: House Price Predictor with California Housing Dataset
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Evaluation metrics: R², RMSE, and business interpretation
Module 3: Classification with Random Forest
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Categorical decisions and classification problems
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Ensemble Learning: the wisdom of the crowd applied to algorithms
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Practical Project: Wine Quality Classifier with UCI dataset
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Advanced metrics: Precision, Recall, and business trade-offs
Module 4: Cross-Validation
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Avoiding self-deception: overfitting and generalization
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K-Fold Cross-Validation for robust estimates
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Systematic comparison of different algorithms
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Evidence-based decision making
DAY 2 – Advanced Techniques and Production
Module 5: Data Preprocessing
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Handling real-world data: missing values, outliers, different scales
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Imputation strategies and feature scaling
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Automated pipelines with scikit-learn
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Preventing data leakage
Module 6: Unsupervised Learning
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Discovering hidden patterns: clustering and segmentation
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K-Means algorithm and optimization
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Practical Project: Customer Segmentation with RFM analysis
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Evaluation without ground truth: Silhouette Score and Elbow Method
Module 7: Hyperparameter Tuning
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Systematic performance optimization
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Grid Search vs Random Search vs Bayesian Optimization
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Learning Curves for advanced diagnostics
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Balancing overfitting/underfitting
Module 8: Deployment and Production
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From Jupyter notebook to production system
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Model persistence and versioning
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Practical Project: REST API for model serving
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Monitoring, A/B testing, and model drift detection
Included Practical Projects
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House Price Predictor – Linear regression on California Housing dataset (20k+ samples)
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Wine Quality Classifier – Random Forest on UCI dataset with feature importance
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Customer Segmentation – K-Means clustering for personalized marketing
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Production API – Flask microservice production-ready with error handling
