Original work in this repository is © Jane Rose Ferdinant, licensed CC BY-NC-ND 4.0 (see LICENSE). The material below is not covered by that license and keeps its own terms. Where a notebook is described as “based on” or “adapted from” a source, that is judged from the notebook’s own text (titles, function names, comments).
This repository was created on GitHub on 2020-08-23 from the GitHub Learning Lab course “Get Started with TensorFlow” (everydeveloper), under the name TensorFlow-Pokemon-Course, and was later renamed Machine-learning-Course. No course files, scaffold or Pokémon data remain in the history; the notebooks are the owner’s own uploads. The credit is given here for the origin only. GitHub Learning Lab shut down on 2022-09-01, so the course homepage no longer resolves.
| Notebook | Based on | Source | License / terms |
|---|---|---|---|
plot_kmeans_silhouette_analysis.ipynb |
scikit-learn example “Selecting the number of clusters with silhouette analysis on KMeans clustering” | https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html | BSD-3-Clause (text below) |
california_regression.ipynb, tip_amount_tensorflow.ipynb |
Google Machine Learning Crash Course programming exercise “Linear regression with a real dataset” (build_model, train_model, plot_the_model and their docstrings), extended by the owner |
https://developers.google.com/machine-learning/crash-course | Apache-2.0 (text below). Google states this license on the exercise notebooks |
titanic-tutorial.ipynb |
Kaggle notebook boilerplate and a feature-engineering workflow that resembles the public Kaggle notebook “Titanic Data Science Solutions” | https://www.kaggle.com/competitions/titanic | Kaggle competition rules and the notebook author’s license apply |
diabetes.ipynb |
A common decision-tree tutorial pattern (column names and the tree export) | Source not recorded | Source not recorded |
SigmoidNeuron.ipynb |
A deep-learning course notebook structure (sigmoid neuron class and toy-data fit) | Source not recorded | Source not recorded |
None of these files is stored in this repository. All thirteen notebooks download their data (or load it with a Keras loader) from the sources below. Where the notebook’s original data file was lost, the notebook now uses the public substitute listed here, and says so in a note at its top; its results differ from the earlier stored outputs.
| Dataset | Used by | Source | License / terms |
|---|---|---|---|
| Banknote Authentication, Lohweg, V. (2012), UCI Machine Learning Repository, https://doi.org/10.24432/C55P57 | banknote_randomforest, ANN_using_tensorflow, ANN_using_pytorch |
https://archive.ics.uci.edu/dataset/267/banknote+authentication | CC BY 4.0 (stated on the UCI page, read 2026-10-13) |
| California housing training set (from the 1990 US census), as distributed with Google’s ML Crash Course | california_regression |
https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv | Distributed by Google with the course; terms per the course page. Original data: Pace and Barry (1997), Sparse spatial autoregressions |
| MNIST handwritten digits, LeCun, Cortes and Burges | mnist_opencv, CNN_Identify_the_Digits_(MNIST) |
Downloaded by keras.datasets.mnist |
Per the MNIST authors’ page (http://yann.lecun.com/exdb/mnist/) |
| Pima Indians Diabetes Database (768 rows). Original owner: National Institute of Diabetes and Digestive and Kidney Diseases; donor: Vincent Sigillito; obtained from UCI | diabetes (substitute for the lost diabetes.csv) |
OpenML data set 37, file https://api.openml.org/data/v1/download/37/diabetes.arff (page https://www.openml.org/d/37). If OpenML is overloaded, the notebook falls back to https://github.com/plotly/datasets/blob/348b89b48db1668e73b413dc56baed489a3ee27c/diabetes.csv, which holds the same numbers (compared on 2026-10-15) | OpenML licence field “Public” (read 2026-10-15). The fallback copy sits in a repository under the MIT licence (https://github.com/plotly/datasets/blob/master/LICENSE, copyright Plotly Technologies Inc.); no separate licence is stated for the table |
| Titanic passenger list (1,309 rows). Authors: Frank E. Harrell Jr. and Thomas Cason, Vanderbilt Biostatistics; principal source: Encyclopedia Titanica | titanic-tutorial (substitute for the Kaggle train.csv and test.csv) |
OpenML data set 40945 (https://www.openml.org/d/40945), loaded with scikit-learn’s fetch_openml; the notebook shuffles it with seed 42 and splits it 891 / 418 |
OpenML licence field “Public” (read 2026-10-15) |
| SMS Spam Collection, Almeida, T. and Hidalgo, J. (2011), UCI Machine Learning Repository, https://doi.org/10.24432/C5CC84 (5,574 messages) | Natural_Language_Processing_with_Disaster_Tweets (substitute for the Kaggle disaster-tweets files) |
https://archive.ics.uci.edu/dataset/228/sms+spam+collection (zip: https://archive.ics.uci.edu/static/public/228/sms+spam+collection.zip); the notebook shuffles it with seed 42 and splits it 80% / 20% | CC BY 4.0 (stated on the UCI page, read 2026-10-15) |
| Restaurant tips table (244 rows). Bryant, P. G. and Smith, M. A. (1995), Practical Data Analysis: Case Studies in Business Statistics, Irwin | tip_amount_tensorflow (substitute for the lost tip-amount.xlsx) |
https://raw.githubusercontent.com/plotly/datasets/79f21048146faece1dd7e9ebc815612d6662c7bc/tips.csv (the plotly/datasets repository; its commit message says “added tips from RStudio”) |
The repository is under the MIT licence (https://github.com/plotly/datasets/blob/master/LICENSE, copyright Plotly Technologies Inc.). No separate licence is stated for the table, so its status beyond that is not known |
| WHO Global Health Observatory, indicator SA_0000001400, “Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol), by beverage type”, 1960 to 2024 (41,521 rows after the notebook drops the “total” rows). World Health Organization | world_alcohol_pandas_revision (substitute for the lost world_alcohol.csv) |
GHO OData API: https://ghoapi.azureedge.net/api/SA_0000001400, with country names from https://ghoapi.azureedge.net/api/DIMENSION/COUNTRY/DimensionValues | CC BY 4.0, “unless specifically indicated otherwise”, per the WHO data terms and conditions (https://data.who.int/about/data/terms-and-conditions, read 2026-10-15) |
diabetes.csv, the Kaggle Titanic and Disaster Tweets files, world_alcohol.csv, tip-amount.xlsx (the original files, no longer used) |
formerly the five notebooks above | Not in the repository. Source of world_alcohol.csv and tip-amount.xlsx not recorded |
Source not recorded for those two. The notebooks no longer use any of them |
NLTK data packages stopwords and wordnet |
Natural_Language_Processing_with_Disaster_Tweets |
Downloaded at run time by nltk.download from the NLTK data server (https://www.nltk.org/nltk_data/) |
Each package carries its own terms (WordNet: the Princeton WordNet licence). The stopwords terms were not checked |
Synthetic data (make_blobs, toy points) |
plot_kmeans_silhouette_analysis, SigmoidNeuron |
Generated in the notebook | n/a |
The notebooks import (not copy) TensorFlow and Keras (Apache-2.0), NumPy, pandas, SciPy, scikit-learn, matplotlib and seaborn (BSD-3-Clause, or compatible permissive licenses), PyTorch (BSD-style), NLTK (Apache-2.0) and gensim (LGPL-2.1). Each keeps its own license; see requirements.txt for the versions used.
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