Mastering Machine Learning

Open courseware covering essential Machine Learning algorithms and applied projects, from data preprocessing to ensemble methods and beyond.

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What you'll learn

• Exploratory data analysis and feature preprocessing
• Evaluate models with the right metrics (accuracy, F1, AUC, confusion matrix)
• Implement and compare classical ML algorithms from scratch
• Handle imbalanced datasets
• Apply dimensionality reduction with PCA
• Build ensemble models (Random Forest, XGBoost)
• Mine association rules with Apriori

Prerequisites

Topic	Level
Python	Basic
Pandas & NumPy	Basic
Statistics fundamentals	Basic

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Contents

All notebooks are in the Projetos/ folder. Datasets are in Bases/.

#	Notebook	Topics
01	Demo1_Algoritmo_Genetico.ipynb	Genetic Algorithms, hyperparameter search
02	Demo2_RegressaoLinear_Boston.ipynb	Linear Regression, Boston Housing
03	Demo3_RF_Churn.ipynb	Random Forest, Churn Prediction
04	Demo4_Comparacao_Metricas.ipynb	Metrics comparison, model evaluation
05	Demo5_KNN_Elbow.ipynb	KNN, elbow method
06	Demo6_KNN_Telco.ipynb	KNN applied to Telco dataset
07	Demo7_K_Means_Clustering.ipynb	K-Means clustering
08	Demo8_Kmeans_Crime_data.ipynb	K-Means on crime data
09	Demo9_Apriori_Movies.ipynb	Association rules, movie recommendations
10	Demo10_RegressaoLogistica.ipynb	Logistic Regression
11	Demo11_Naive_Bayes.ipynb	Naive Bayes classifier
12	Demo12_PCA.ipynb	Principal Component Analysis
13	Demo13_PCA_SVM.ipynb	PCA + Support Vector Machines
14	Demo14_Ensemble_Techniques.ipynb	Bagging, Boosting, Stacking
15	Demo15_XGBoost.ipynb	XGBoost, gradient boosting
16	Demo16_Datasets_Desbalanceados.ipynb	Imbalanced datasets, SMOTE

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Getting Started

Option 1 — Google Colab (recommended)

Open any notebook directly on GitHub and click Open in Colab at the top of the file.

Option 2 — Local

git clone https://github.com/ahirtonlopes/Mastering-Machine-Learning.git
cd Mastering-Machine-Learning
python -m venv .venv &&source .venv/bin/activate
pip install scikit-learn pandas numpy matplotlib xgboost imbalanced-learn jupyter
jupyter notebook

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Suggested Learning Path

Beginner → Demos 2, 5, 7, 10, 11
Intermediate → Demos 3, 4, 6, 8, 12, 13
Advanced → Demos 1, 9, 14, 15, 16

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Author

Prof. Dr. Ahirton Lopes · LinkedIn · Google Scholar

Contributions are welcome — open an issue or submit a pull request.

License

MIT