AI Learning Resources

Guide to useful AI/ML resources

Choosing an ML learning resource is primarliy a matter of personal preference. In geneal, I recommend that you refer to more than one resource (other than scikit-learn, tensorflow, and PyTorch documentation) when learning ML.

If you have an .edu email account you can get free access to oreilly.com which has some good books for beginners as well as advanced books on various AI/ML topics.

Here are some books on oreilly.com that may be helpful:

Here are some articles and other resources for developers:

I have found some of the books in the Pragmatic Bookshelf series (available on oreilly.com) to be helpful if you already have extensive software engineering experience:

Here are some other books on oreilly.com that I have found helpful:

Here are some textbooks that I have found helpful:

M. P. Deisenroth, A. A. Faisal, and C. S. Ong, Mathematics for Machine Learning, Cambridge, UK: Cambridge University Press, ISBN: 978–1–108–47004–9, 2020.

R. H. Arpaci-Dusseau and A. C. Arpaci-Dusseau, Operating Systems: Three Easy Pieces, 2018, v. 1.01, Available online: https://pages.cs.wisc.edu/~remzi/OSTEP/

W. McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2nd ed., O’Reilly Media, ISBN: 978–1491957660, 2017.

S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Upper Saddle River, NJ: Prentice Hall, ISBN: 978–0–13–604259–4, 2021.

E. Alpaydin, Introduction to Machine Learning, 3rd ed., MIT Press, ISBN: 978–0262028189, 2014.

S. Raschka. and V. Mirjalili, Python Machine Learning, 2nd ed. Packt, ISBN: 978–1787125933, 2017.

S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python — Analyzing Text with the Natural Language Toolkit. Available online at https://www.nltk.org/book/

B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo, Robotics: Modeling, Planning and Control, London: Springer, ISBN: 978–1–84628–641–4, 2010.

J. Zobel, Writing for Computer Science, 3rd ed., London: Springer, ISBN: 978–1–4471–6638–2, 2014.

Here are some resources on various AI/ML topics that I have found useful:

K. NG, A. Padmanabhan, and M. R. Cole, Mobile Artificial Intelligence Projects, Birmingham, U.K.: Packt Pub. Ltd., 2019.

F. X. Govers, Artificial Intelligence for Robotics, Birmingham, U.K.: Packt Pub. Ltd., 2018.

V. Lakshmanan, S. Robinson, M. Munn, Machine Learning Design Patterns, Sebastopol, CA: O’Reilly Media, Inc., 2021.

P. Palanisamy, Hands-On Intelligent Agents with OpenAI Gym, Birmingham, U.K.: Packt Pub. Ltd., 2018.

H. Lin and B. Biggio, “Adversarial Machine Learning: Attacks From Laboratories to the Real World,” IEEE Computer, May 2021.

aima-python: Python code for Artificial Intelligence: A Modern Approach. Accessed: June 14, 2020. [Online]. Available: https://github.com/aimacode/aima-python, 0.17.3. 2020.

Python Machine Learning Code Repository, Accessed: 11/14/2021. [Online]. Available Online: https://github.com/rasbt/python-machine-learning-book-3rd-edition

I am an AI Software Engineer with M.S. in Mathematics and MSCS in Aritifical Intelligence and 25+ years of software engineering experence.