1. Курсова та навчальна література онлайн
1.1. The Elements of Statistical Learning / Trevor Hastie, Robert Tibshirani, Jerome Friedman. Stanford University, 2021.
URL:https://web.stanford.edu/~hastie/ElemStatLearn/
1.2. An Introduction to Statistical Learning (ISL) / Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. 2nd ed., 2021.
URL:https://www.statlearning.com/
1.3. Probabilistic Machine Learning Book / Kevin P. Murphy. MIT Press, 2022.
URL:https://probml.github.io/pml-book/
1.4. Network Science / Albert-László Barabási. Cambridge University Press, 2021 (online edition).
URL:http://networksciencebook.com/
1.5. Deep Learning Book / Ian Goodfellow, Yoshua Bengio, Aaron Courville. MIT Press, 2016.
URL:https://www.deeplearningbook.org/
2. Офіційна документація інструментів і бібліотек
2.1. NumPy Documentation.
URL:https://numpy.org/doc/
2.2. pandas Documentation.
URL:https://pandas.pydata.org/docs/
2.3. scikit-learn Documentation.
URL:https://scikit-learn.org/stable/
2.4. Matplotlib Documentation.
URL:https://matplotlib.org/stable/
2.5. Seaborn Documentation.
URL:https://seaborn.pydata.org/
2.6. NetworkX Documentation.
URL:https://networkx.org/documentation/stable/
2.7. Statsmodels Documentation.
URL:https://www.statsmodels.org/stable/
2.8. TensorFlow Documentation.
URL:https://www.tensorflow.org/
2.9. Keras Documentation.
URL:https://keras.io/
2.10. PyTorch Documentation.
URL:https://pytorch.org/docs/stable/
2.11. Prophet Documentation (Meta AI).
URL:https://facebook.github.io/prophet/
3. Лекції, онлайн-курси, відеоматеріали
3.1. MIT OpenCourseWare: Machine Learning. Massachusetts Institute of Technology.
URL:https://ocw.mit.edu/courses/6-036-introduction-to-machine-learning-fall-2020/
3.2. Harvard CS109A: Data Science. Harvard University.
URL:https://cs109a.github.io/
3.3. DeepLearning.AI Courses. Andrew Ng, Coursera.
URL:https://www.deeplearning.ai/
3.4. Fast.ai: Practical Deep Learning for Coders.
URL:https://course.fast.ai/
4. Репозиторії даних та інструменти для практики
4.1. UCI Machine Learning Repository.
URL:https://archive.ics.uci.edu/
4.2. Kaggle Datasets and Competitions.
URL:https://www.kaggle.com/
4.3. Google Dataset Search.
URL:https://datasetsearch.research.google.com/
4.4. OpenML Repository.
URL:https://www.openml.org/
5. Онлайн-інструменти для аналізу та візуалізації
5.1. Google Colab — Cloud Python Notebook Environment.
URL:https://colab.research.google.com/
5.2. Jupyter Project.
URL:https://jupyter.org/
5.3. Observable (Data Visualization Tools).
URL:https://observablehq.com/