AI Learning Resources

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Outline

Overview

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. The article Creating an Account describes what to do if your school is not in the list

Quickstart for Beginners

Be sure to see Prerequisite Textbooks below.

Linux Resources

Even if you are just learning AI/ML, you should be using Linux.

Articles for Beginners

Articles for Developers

Here are some articles and other resources for developers:

Books for Beginners

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

Books 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:

Prerequisite Textbooks

K. Rosen, Discrete Mathematics and Its Applications, 8th ed., McGraw Hill, ISBN: 978–1–259–67651–2, 2019.

Either of the following two textbooks are a good math refresher which should not be your first exposure to the covered topics:

J. Fanchi, Math Refresher for Scientists and Engineers, 3rd ed., Wiley, ISBN: 0–471–75715–2, 2006.

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.

These textbooks cover topics that are usually required for a degree in computer science:

R. V. Hogg, J. W. McKean, and A. T. Craig, Introduction to Mathematical Statistics, Pearson, ISBN 0134686993, 2019.

C. Hamacher, Z. Vranesic, S. Zaky, and N. Manjikian, Computer Organization and Embedded Systems, 6th ed., McGraw Hill, ISBN: 978 — 0 — 07 — 338065 — 0, 2012.

A. S. Tanenbaum and D. J. Wetherall, Computer Networks, 5th ed., Pearson, ISBN: 0–13–212695–8, 2011.

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/

Here are some landmark textbooks and references on software engineering best practices which are highly recommended:

S. McConnell, Code Complete, 2nd ed., Microsoft Press, ISBN: 0–7356–1967–0, 2004.

M. Howard and D. LeBlanc, Writing Secure Code, 2nd ed., Microsoft Press Press, ISBN: 0735617228, 2003.

P. Bourque and R. E. Fairley, Guide to the Software Engineering Body of Knowledge, v. 3, IEEE, 2014.

Textbooks

Here are some textbooks that I have found helpful:

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

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.

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

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

D. Jurafsky and J. H. Martin, 2nd edition. Speech and Language Processing. Prentice Hall, ISBN: 978–0131873216, 2008.

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.

Resources on Advanced Concepts

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.

Code Repos

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

AI News Feeds

Here are some RSS feeds that I have found helpful using the NetNewsWire iOS app:

Here are some medium RSS feeds that I find most useful:

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Jeff Holmes MS MSCS

Jeff Holmes MS MSCS

I am an AI Engineer with an M.S. in Mathematics and MSCS in Artificial Intelligence with 30 years of software engineering experence.