{"id":30861101,"url":"https://github.com/gperdrizet/datascience-ml-teaching","last_synced_at":"2025-09-07T16:48:05.647Z","repository":{"id":312559704,"uuid":"1047869942","full_name":"gperdrizet/datascience-ML-teaching","owner":"gperdrizet","description":"Data science and machine learning teaching materials portfolio","archived":false,"fork":false,"pushed_at":"2025-08-31T14:15:36.000Z","size":21,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-31T15:32:49.352Z","etag":null,"topics":["data-science","education","machine-learning","python"],"latest_commit_sha":null,"homepage":"https://gperdrizet.github.io/datascience-ML-teaching/","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/gperdrizet.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-08-31T12:25:23.000Z","updated_at":"2025-08-31T14:19:24.000Z","dependencies_parsed_at":"2025-08-31T15:32:51.533Z","dependency_job_id":"13df7b99-654f-491d-b104-70d774c14fbf","html_url":"https://github.com/gperdrizet/datascience-ML-teaching","commit_stats":null,"previous_names":["gperdrizet/datascience-ml-teaching"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/gperdrizet/datascience-ML-teaching","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gperdrizet%2Fdatascience-ML-teaching","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gperdrizet%2Fdatascience-ML-teaching/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gperdrizet%2Fdatascience-ML-teaching/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gperdrizet%2Fdatascience-ML-teaching/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gperdrizet","download_url":"https://codeload.github.com/gperdrizet/datascience-ML-teaching/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gperdrizet%2Fdatascience-ML-teaching/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":274065728,"owners_count":25216444,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-07T02:00:09.463Z","response_time":67,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","education","machine-learning","python"],"created_at":"2025-09-07T16:48:04.305Z","updated_at":"2025-09-07T16:48:05.625Z","avatar_url":"https://github.com/gperdrizet.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data science \u0026 ML teaching materials\n\n[![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)\n\nData science and machine learning teaching materials portfolio\n\n| Topic | Slides | Videos | Project | Dataset | Tools/Libraries | Learning Goals |\n|-------|--------|--------|---------|---------|----------------|----------------|\n| 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 |\n| 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 |\n| 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 |\n| 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 |\n| Decision trees \u0026 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 \u0026 pruning techniques, overfitting mitigation, ensemble methods feature importance, tree visualization, hyperparameter optimization |\n| 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 |\n| 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 |\n| 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 |\n| 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 |\n| 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 |\n| 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 |\n| 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 |","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgperdrizet%2Fdatascience-ml-teaching","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgperdrizet%2Fdatascience-ml-teaching","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgperdrizet%2Fdatascience-ml-teaching/lists"}