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ADD: All resources to become Ai engineer, with free courses and yt video #12396 (#12560)

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* ADD: All resources to become Ai engineer, with free courses and yt videos  #12396

* Update free-programming-books-subjects.md

* Update links and formatting in subjects list

* Remove duplicate book entries in subjects list

Removed duplicate entries for 'Introduction to Machine Learning Systems' and 'Learn Tensorflow'.

* Fix formatting for text mining and ML resources

* Update books/free-programming-books-subjects.md

Co-authored-by: Eric Hellman <eric@hellman.net>

* Update books/free-programming-books-subjects.md

Co-authored-by: Eric Hellman <eric@hellman.net>

* Update books/free-programming-books-subjects.md

Co-authored-by: Eric Hellman <eric@hellman.net>

* Remove LLM Transformer Model link from subjects

Removed a link to 'LLM Transformer Model Visually Explained'.

* Add LLM Transformer Model tutorial link

---------

Co-authored-by: Eric Hellman <eric@hellman.net>
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books/free-programming-books-subjects.md
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@@ -149,12 +149,16 @@ Books that cover a specific programming language can be found in the [BY PROGRAM
* [Generative AI in Higher Education: The ChatGPT Effect](https://www.taylorfrancis.com/books/oa-mono/10.4324/9781003459026/generative-ai-higher-education-cecilia-ka-yuk-chan-tom-colloton) - Cecilia Ka Yuk Chan, Tom Colloton (PDF) (CC BY)
* [Getting Started with Artificial Intelligence , 2nd Edition](https://www.ibm.com/downloads/cas/OJ6WX73V) - Tom Markiewicz, Josh Zheng (PDF)
* [Graph Representational Learning Book](https://www.cs.mcgill.ca/~wlh/grl_book/) - William L. Hamilton
* [How Transformer LLMs Work](https://www.deeplearning.ai/short-courses/how-transformer-llms-work/) - Jay Alammar, Maarten Grootendorst (DeepLearning.AI)
* [Hugging Face LLM Course](https://huggingface.co/learn/llm-course/en/chapter1/1) (HTML)
* [Introduction to Autonomous Robots](https://github.com/correll/Introduction-to-Autonomous-Robots/releases) - Nikolaus Correll (PDF) (CC BY-NC-ND)
* [Large Language Models from Scratch](https://www.youtube.com/watch?v=lnA9DMvHtfI) - freeCodeCamp (YouTube)
* [Machine Learning For Dummies®, IBM Limited Edition](https://www.ibm.com/downloads/cas/GB8ZMQZ3) - Daniel Kirsch, Judith Hurwitz (PDF)
* [Mastering Generative AI and Prompt Engineering: A Practical Guide for Data Scientists](https://datasciencehorizons.com/pub/Mastering_Generative_AI_Prompt_Engineering_Data_Science_Horizons_v2.pdf) - Data Science Horizons (PDF)
* [On the Path to AI: Law’s prophecies and the conceptual foundations of the machine learning age](https://link.springer.com/book/10.1007/978-3-030-43582-0) - Thomas D. Grant, Damon J. Wischik (PDF, EPUB)
* [Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp](https://github.com/norvig/paip-lisp) - Peter Norvig
* [Probabilistic Programming & Bayesian Methods for Hackers](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) - Cam Davidson-Pilon (HTML, Jupyter Notebook)
* [Stanford CS224N: Natural Language Processing with Deep Learning](https://www.youtube.com/playlist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ) - Christopher Manning (Stanford Online)
* [The History of Artificial Intelligence](https://courses.cs.washington.edu/courses/csep590/06au/projects/history-ai.pdf) - Chris Smith, Brian McGuire, Ting Huang, Gary Yang (PDF)
* [The Quest for Artificial Intelligence: A History of Ideas and Achievements](https://ai.stanford.edu/~nilsson/QAI/qai.pdf) - Nils J. Nilsson (PDF)
@@ -279,8 +283,9 @@ Books that cover a specific programming language can be found in the [BY PROGRAM
* [A Programmer's Guide to Data Mining](http://guidetodatamining.com) - Ron Zacharski (Draft)
* [Data Jujitsu: The Art of Turning Data into Product](http://www.oreilly.com/data/free/data-jujitsu.csp) (email address *requested*, not required)
* [Data Mining Algorithms In R](https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R) - Wikibooks
* [Data Mining Concepts and Techniques](https://ia800702.us.archive.org/7/items/datamining_201811/DS-book%20u5.pdf) - Jiawei Han, Micheline Kamber, Jian Pei (PDF) ( :card_file_box: archived)
* [Data Mining Concepts and Techniques](https://ia800702.us.archive.org/7/items/datamining_201811/DS-book%20u5.pdf) - Jiawei Han, Micheline Kamber, Jian Pei (PDF) *( :card_file_box: archived)*
* [Data Science at the Command Line](https://datascienceatthecommandline.com/2e/) - Jeroen Janssens
* [Data Science with Python](https://www.youtube.com/watch?v=LHBE6Q9XlzI) - freeCodeCamp
* [Elements of Data Science](https://allendowney.github.io/ElementsOfDataScience/README.html) - Allen B. Downey
* [Feature Engineering and Selection: A Practical Approach for Predictive Models](https://bookdown.org/max/FES/) - Max Kuhn, Kjell Johnson
* [Foundations of Data Science](https://www.cs.cornell.edu/jeh/book.pdf) - Avrim Blum, John Hopcroft, Ravindran Kannan (PDF)
@@ -288,11 +293,13 @@ Books that cover a specific programming language can be found in the [BY PROGRAM
* [Hands-On Data Visualization](https://handsondataviz.org) - Jack Dougherty, Ilya Ilyankou (HTML)
* [High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications](https://book-wright-ma.github.io/Book-WM-20210422.pdf) - John Wright, Yi Ma (PDF)
* [Internet Advertising: An Interplay among Advertisers, Online Publishers, Ad Exchanges and Web Users](https://arxiv.org/pdf/1206.1754v2.pdf) (PDF)
* [Introduction to Cultural Analytics & Python](https://melaniewalsh.github.io/Intro-Cultural-Analytics/welcome.html) - Melanie Walsh
* [Introduction to Cultural Analytics & Python](https://melaniewalsh.github.io/Intro-Cultural-Analytics/welcome.html) - Melanie Walsh
* [Introduction to Data Science](https://docs.google.com/file/d/0B6iefdnF22XQeVZDSkxjZ0Z5VUE/edit?pli=1) - Jeffrey Stanton
* [Introduction to Data Science in Python](https://www.coursera.org/learn/python-data-analysis) - Christopher Brooks (University of Michigan) (Coursera)
* [Julia Data Science](https://juliadatascience.io) - Jose Storopoli, Rik Huijzer, Lazaro Alonso (HTML) (CC BY-NC-SA 4.0)
* [Mining of Massive Datasets](http://infolab.stanford.edu/~ullman/mmds/book.pdf) - Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman (PDF)
* [Probability and Statistics with Examples using R](https://www.isibang.ac.in/~athreya/psweur/index.html#usage) - Siva Athreya, Deepayan Sarkar, Steve Tanner (HTML) ( :construction: *in process*)
* [Probability and Statistics with Examples using R](https://www.isibang.ac.in/~athreya/psweur/index.html#usage) - Siva Athreya, Deepayan Sarkar, Steve Tanner (HTML) *( :construction: *in process*)*
* [Python for Data Science – Hands-on Projects](https://www.youtube.com/watch?v=vaMMLLf0wLM) - LunarTech (freeCodeCamp) (YouTube)
* [School of Data Handbook](https://schoolofdata.org/handbook/)
* [Statistical inference for data science](https://leanpub.com/LittleInferenceBook/read) - Brian Caffo
* [The Ultimate Guide to 12 Dimensionality Reduction Techniques (with Python codes)](https://www.analyticsvidhya.com/blog/2018/08/dimensionality-reduction-techniques-python/) - Pulkit Sharma
@@ -422,9 +429,11 @@ Books that cover a specific programming language can be found in the [BY PROGRAM
* [Approaching Almost Any Machine Learning Problem](https://github.com/abhishekkrthakur/approachingalmost) - Abhishek Thakur (PDF)
* [Automated Machine Learning - Methods, Systems, Challenges](https://link.springer.com/book/10.1007/978-3-030-05318-5) - Frank Hutter, Lars Kotthoff, Joaquin Vanschoren (PDF, EPUB)
* [Bayesian Reasoning and Machine Learning](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage)
* [CS50's Introduction to Artificial Intelligence with Python](https://cs50.harvard.edu/ai/) - David Malan (Harvard)
* [Deep Learning](https://www.deeplearningbook.org) - Ian Goodfellow, Yoshua Bengio, Aaron Courville
* [Deep Learning for Coders with Fastai and PyTorch](https://github.com/fastai/fastbook) - Jeremy Howard, Sylvain Gugger (Jupyter Notebooks)
* [Dive into Deep Learning](https://d2l.ai)
* [Elements of AI](https://www.elementsofai.com) - MinnaLearn, University of Helsinki
* [Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises](https://web.stanford.edu/group/pdplab/pdphandbook) - James L. McClelland
* [Foundations of Machine Learning, Second Edition](https://mitpress.ublish.com/ebook/foundations-of-machine-learning--2-preview/7093/Cover) - Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
* [Free and Open Machine Learning](https://nocomplexity.com/documents/fossml/) - Maikel Mardjan (HTML)
@@ -439,6 +448,7 @@ Books that cover a specific programming language can be found in the [BY PROGRAM
* [Learn Tensorflow](https://bitbucket.org/hrojas/learn-tensorflow) - Jupyter Notebooks
* [Learning Deep Architectures for AI](https://mila.quebec/wp-content/uploads/2019/08/TR1312.pdf) - Yoshua Bengio (PDF)
* [Machine Learning](https://www.intechopen.com/books/machine_learning)
* [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course) - Google (HTML)
* [Machine Learning for Beginners](https://github.com/Microsoft/ML-For-Beginners) - Microsoft
* [Machine Learning for Data Streams](https://moa.cms.waikato.ac.nz/book-html/) - Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
* [Machine Learning from Scratch](https://dafriedman97.github.io/mlbook/) - Danny Friedman (HTML, PDF, Jupyter Book)
@@ -450,7 +460,7 @@ Books that cover a specific programming language can be found in the [BY PROGRAM
* [Neural Network Design (2nd Edition)](https://hagan.okstate.edu/NNDesign.pdf) - Martin T. Hagan, Howard B. Demuth, Mark H. Beale, Orlando De Jesús (PDF)
* [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com)
* [Pattern Recognition and Machine Learning](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf) - Christopher M. Bishop (PDF)
* [Patterns, Predictions, And Actions: A story about machine learning](https://mlstory.org/pdf/patterns.pdf) - Moritz Hardt, Benjamin Recht (PDF)
* [Patterns, Predictions, And Actions: A story about machine learning](https://mlstory.org/pdf/patterns.pdf) - Moritz Hardt, Benjamin Recht (PDF)
* [Practitioners guide to MLOps](https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf) - Khalid Samala, Jarek Kazmierczak, Donna Schut (PDF)
* [Probabilistic Machine Learning - An Introduction](https://github.com/probml/pml-book/releases/latest/download/book1.pdf) - Kevin P. Murphy (PDF)
* [Probabilistic Models in the Study of Language](https://idiom.ucsd.edu/~rlevy/pmsl_textbook/text.html) (Draft, with R code)

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@@ -131,6 +131,7 @@
* [Data Science Tutorial for Beginners - Video Course](https://www.scaler.com/topics/course/python-for-data-science/) - course by Yash Sinha Scaler Topics
* [Essential Linear Algebra for Data Science and Machine Learning](https://www.kdnuggets.com/2021/05/essential-linear-algebra-data-science-machine-learning.html) - KDnuggets
* [Interactive Linear Algebra](https://textbooks.math.gatech.edu/ila/) - Dan Margalit, Joseph Rabinoff (HTML, PDF)
* [LLM Transformer Model Visually Explained](https://poloclub.github.io/transformer-explainer/) - Georgia Tech (HTML)
* [Top 10 Data Science Projects for Beginners - KDnuggets](https://www.kdnuggets.com/2021/06/top-10-data-science-projects-beginners.html)