https://github.com/gperdrizet/datascience-ml-teaching

Data science and machine learning teaching materials portfolio
https://github.com/gperdrizet/datascience-ml-teaching

data-science education machine-learning python

Last synced: 10 months ago
JSON representation

Data science and machine learning teaching materials portfolio

• Host: GitHub
• URL: https://github.com/gperdrizet/datascience-ml-teaching
• Owner: gperdrizet
• License: gpl-3.0
• Created: 2025-08-31T12:25:23.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2025-08-31T14:15:36.000Z (11 months ago)
• Last Synced: 2025-08-31T15:32:49.352Z (11 months ago)
• Topics: data-science, education, machine-learning, python
• Homepage: https://gperdrizet.github.io/datascience-ML-teaching/
• Size: 20.5 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Data science & ML teaching materials

[![pages-build-deployment](https://github.com/gperdrizet/datascience-ML-teaching/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/gperdrizet/datascience-ML-teaching/actions/workflows/pages/pages-build-deployment)

Data science and machine learning teaching materials portfolio

| Topic | Slides | Videos | Project | Dataset | Tools/Libraries | Learning Goals |

|-------|--------|--------|---------|---------|----------------|----------------|

| Logistic regression | [PDF](slides/15-intro_to_ML-logistic_regression.pdf) | [Part I](https://youtu.be/vdCOt6sx6OQ?si=8ZzY3eEhXjovjAKG) • [Part II](https://youtu.be/1E2v33ZJ5HY?si=6IcjKWLUFNCqmcws) | [GitHub](https://github.com/gperdrizet/logistic-regression) | Banking marketing campaign dataset (48,895 records with customer demographics, financial history, and campaign outcomes) | Python, pandas, scikit-learn, matplotlib, seaborn, numpy | Binary classification, hyperparameter optimization with GridSearchCV, confusion matrix analysis, threshold tuning |

| Data preprocessing | [PDF](slides/17-EDA.pdf) | [Part I](https://youtu.be/28oflJeJMqI?si=4fKv8NjJo_rFm8DG) • [Part II](https://youtu.be/cQxeS6ZXxzs?si=1RyCOK2orwGEu_kU) | [GitHub](https://github.com/gperdrizet/data-preprocessing) | AirBnB NYC 2019 dataset (48,895 listings with price, location, room type, host info, review data) | Python, pandas, numpy, matplotlib, seaborn, scikit-learn, scipy | Data cleaning, statistical analysis, feature relationships with Chi-squared and Kruskal-Wallis tests, missing value imputation, categorical encoding, Box-Cox transformation |

| Linear regression | [PDF](slides/19-linear_regression.pdf) | [YouTube](https://youtu.be/HvAni4pZ5-g?si=TEXJrDvbelkTJIYl) | [GitHub](https://github.com/gperdrizet/linear-regression) | Medical insurance cost dataset (1,338 policyholders with demographics, BMI, smoking status, region) | Python, pandas, numpy, scikit-learn, matplotlib, seaborn | Linear relationships, least squares estimation, feature engineering, polynomial features, model evaluation metrics, class imbalance with over-sampling |

| Regularized linear regression | [PDF](slides/19-linear_regression.pdf) | [YouTube](https://youtu.be/HvAni4pZ5-g?si=TEXJrDvbelkTJIYl) | [GitHub](https://github.com/gperdrizet/regularized-linear-regression) | US county-level sociodemographic and health data (2018-2019) for morbidity prediction | Python, pandas, numpy, scikit-learn, matplotlib, seaborn | Ridge and Lasso regression (L1/L2 regularization), overfitting prevention, hyperparameter tuning, polynomial feature engineering, bias-variance tradeoff |

| Decision trees & ensemble methods | [PDF](slides/21-decision_trees.pdf) | [Part I](https://youtu.be/YjkMOjdJiQI?si=q3CYZk7zM5MIcUhX) • [Part II](https://youtu.be/zILRAjkr7SU?si=hbBLTHpKgdGKZcvN) • [Part III](https://youtu.be/0onoMLVzKWE?si=pHhTKM5vl9nRTBZj) | [GitHub](https://github.com/gperdrizet/decisiontrees-ensemble-methods) | Diabetes physiology dataset (biomedical features from 768 patients with binary diabetes label) | Python, pandas, scikit-learn, matplotlib | Decision tree construction & pruning techniques, overfitting mitigation, ensemble methods feature importance, tree visualization, hyperparameter optimization |

| Naive Bayes | [PDF](slides/23-naive_Bayes.pdf) | [YouTube](https://youtu.be/hWGyUHFztiA?si=Nu-he-7r0kT3pmxh) | [GitHub](https://github.com/gperdrizet/naive-bayes) | Google Play Store app reviews dataset for sentiment analysis (positive/negative polarity) | Python, pandas, numpy, scikit-learn, NLTK, matplotlib, seaborn, scipy | Text preprocessing with lemmatization, multiple Naive Bayes variants comparison, dimensionality reduction with PCA and Feature Agglomeration, cross-validation, NLP techniques |

| K-nearest neighbors | [PDF](slides/13-KNN.pdf) | [YouTube](https://youtu.be/zQe6WRLFYkE?si=qXzd8Y8e2qTdoWKe) | [GitHub](https://github.com/gperdrizet/k-nearest-neighbors) | Red wine quality dataset (4,898 wine samples with chemical composition features and quality ratings from 0-10) | Python, pandas, numpy, scikit-learn, matplotlib | Distance metrics (Euclidean, Manhattan), k-value selection, nearest neighbor voting, model performance evaluation with classification/regression metrics, computational complexity considerations |

| K-means clustering | [PDF](slides/25-unsupervised_learning.pdf) | [YouTube](https://youtu.be/szy8kSvOxSI?si=BM_qFisqGCpZ5xgX) | [GitHub](https://github.com/gperdrizet/k-means) | California housing dataset (20,640 records with geographic coordinates and median income) | Python, pandas, scikit-learn, numpy, matplotlib, seaborn, plotly | Unsupervised learning, clustering algorithms for market segmentation, geographic data visualization, supervised classification for cluster prediction, 2D and 3D visualization |

| Time series forecasting | [PDF](slides/26-time_series_forecasting.pdf) | [YouTube](https://youtu.be/I2NOt6HUMp4?si=zyzKRrIrR3SQmJrM) | [GitHub](https://github.com/gperdrizet/time-series) | Airline Passengers dataset from Seaborn (1949-1960 monthly passenger counts with seasonal patterns) | Python, pandas, numpy, matplotlib, seaborn, scikit-learn, pmdarima, statsmodels, scipy | Time series analysis, stationarity testing, baseline models, ARIMA modeling with auto_arima, TimeSeriesSplit validation, trend and seasonality analysis |

| Image classification | [PDF](slides/27-deep_learning_partI.pdf) | [Part I](https://youtu.be/Ml5LepY-Uk4?si=XTAe7FhfW1o0zHJb) • [Part II](slides/28-deep_learning_partII.pdf) | [GitHub](https://github.com/gperdrizet/image-classification) | Dogs vs Cats dataset from Kaggle competition (image classification with Kaggle API integration) | Python, TensorFlow/Keras, numpy, matplotlib, kaggle API, Inception-V3 | Convolutional Neural Networks, deep learning, image preprocessing, model training with GPU, hyperparameter optimization, fine-tuning Kaggle API usage, binary image classification |

| Natural language processing | [PDF](slides/29-NLP.pdf) | [YouTube](https://youtu.be/2RuIlOmkrdw?si=itan0rL3xfd5ugpe) | [GitHub](https://github.com/gperdrizet/natural-language-processing) | URL dataset for binary classification (spam detection) | Python, pandas, numpy, scikit-learn, matplotlib, seaborn, NLTK | Text preprocessing, tokenization, TF-IDF vectorization, NLP pipeline development, support vector machines/classifiers |

| Recommender systems | [PDF](slides/30-recommender_systems.pdf) | [YouTube](https://youtu.be/6FWhLhSZPww?si=5_m0vN5a3-5i3eQK) | [GitHub](https://github.com/gperdrizet/recommender-systems) | IMDB movie database (4803 movies with text features like description, genera, keywords and cast names) | Python, pandas, scikit-learn, NLTK, matplotlib | Text preprocessing, tokenization, TF-IDF vectorization, NLP pipeline development, k-nearest-neighbors |