Інтелектуальний аналіз даних

1. Géron A. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. 3rd ed. O’Reilly Media, 2023. 856 p.
URL:https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/

2. 
Milad Vazan. Machine Learning and Data Science: Foundations, Concepts, Algorithms, and Tools. Shahid Beheshti University,  2022.URL:https://www.researchgate.net/publication/358519598_Machine_Learning_and_Data_Science_Foundations_Concepts_Algorithms_and_Tools 

3. Han J., Pei J., Kamber M. Data Mining: Concepts and Techniques. 4th ed. Morgan Kaufmann, 2023. 807 p.
URL:https://www.elsevier.com/books/data-mining/han/978-0-12-811760-6

4. James G., Witten D., Hastie T., Tibshirani R. An Introduction to Statistical Learning. 2nd ed. Springer, 2021. 608 p.
URL:https://www.statlearning.com/

5. Murphy K. P. Probabilistic Machine Learning: An Introduction. MIT Press, 2022. 1600 p.
URL:https://probml.github.io/pml-book/book1.html

6. Barabási A.-L. Network Science. Cambridge University Press, 2021 (updated online edition).
URL:http://networksciencebook.com/

7. Chollet F. Deep Learning with Python. 2nd ed. Manning Publications, 2021. 504 p.
URL:https://www.manning.com/books/deep-learning-with-python-second-edition

8. McKinney W. Python for Data Analysis. 3rd ed. O’Reilly Media, 2022. 550 p. URL:https://wesmckinney.com/book/

9. VanderPlas J. Python Data Science Handbook. 2nd ed. O’Reilly Media, 2022. 700 p.
URL:https://jakevdp.github.io/PythonDataScienceHandbook/

10. Prophet Team. Forecasting at Scale: Prophet Documentation. Meta AI, 2023.
URL:https://facebook.github.io/prophet/

11. NetworkX Developers. NetworkX Documentation. Version 3.x, 2023–2024.
URL:https://networkx.org/documentation/stable/

12. The Elements of Statistical Learning (Hastie, Tibshirani, Friedman). Stanford University, 2021.
URL:https://web.stanford.edu/~hastie/ElemStatLearn/

20. Pattern Recognition and Machine Learning (Bishop C. M.). Springer, 2006 (вільна версія 2022).
URL: https://prml.github.io/

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/