Self learning AI / Data Science curriculum.
This repository is intended to provide Artificial Intelligence, Machine Learning, and Deep Learning resources based on the recommendations of Eng. Mohamed Hammad.
Each resource is accompanied by the post at which it got recommended; so that you won't feel lost choosing one over the other, Make sure to check them out before beginning your journey.
The referring links for the [Book]s are being updated gradually.
You could download [Book]s directly from Library Genesis.
- MIT's Introduction to Computer Science and Programming Using Python [Tutorial] [Beginner]
- Stanford - Programming Paradigms [Tutorial] [Post Link] [Beginner]
- Grokking Algorithms [Book] [Post link] [Beginner]
- Algorithms Unplugged [Book] [Post 1], [Post 2], [Post 3] [Advanced]
- MIT | Matrix Methods in Data Analysis, Signal Processing, and Machine Learning [Tutorial ] [Post link]
- Mathematics for Machine Learning [Tutorial ] [Post link]
- Discrete Mathematics and Its Applications - 7th Edition- [Chapter 10 - Graphs] [Post link]
- Probability For Dummies [Book] [Post link] [Beginner]
- An Introduction to Statistical Learning [Book] [Tutorial] [Post link] [Intermediate]
- The Art of Statistics: Learning from Data [Book] [Post link]
- The Elements of Statistical Learning [Book] [Post link] [Advanced]
- Probabilistic Machine Learning [Kevin Murphy] [Book] [2012 Edition] [2022 Edition] [Post link]
- An Introduction to Statistical Learning [Book] [Post link] [New 2024 ๐ฅ] [Beginner]
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Planting the Seeds of Probabilistic Thinking (Learn Probability role in Machine Learning) [Tutorial] [Beginner] [Post link]
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ANU Statistical Machine Learning 2022 COMP4670/8600 [Tutorial] [Post link]
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[First Check This Post] [Date:
August 2022]
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[First Check This Post] [Date:
August 2021]
- Eng. Hammad ITI Lecture
- MIT RES.6-012 Introduction to Probability, Spring 2018 [Tutorial]
- UC Berkeley CS 188 Introduction to Artificial Intelligence, Fall 2018 [Tutorial] [Arabic Edition]
- Stanford CS221: Artificial Intelligence: Principles and Techniques [Lecture 11 to 15 only][Tutorial] [Post Link]
- Daphne Koller - Probabilistic Graphical Models [Courses] [Post link]
- Probabilistic Machine Learning: An Introduction [Kevin Murphy] (2022) [Book] [Post 1] [Post 2]
- Gaussian Processes for Machine Learning, MIT Press [Book]
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[First Check This Post] [Date:
June 2020]
- Machine Learning Fundamentals [Tutorial] [Unavailable now]
- Undergraduate machine learning at UBC 2012 [Tutorial] [Slides]
- CS480/680: Intro to Machine Learning - Spring 2019 - University of Waterloo [Tutorial] [Post 1], [Post 2]
- Machine Learning 2013 [Tutorial]
- Bayesian Reasoning and Machine Learning [Book]
- Python Data Science Handbook [Book]
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[First Check This Post] [Date:
September 2021]
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SYDE 522: Machine Intelligence (Winter 2018, University of Waterloo) [Tutorial] [Lecture notes] [Post 1], [Post 2]
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CS480/680: Intro to Machine Learning - Spring 2019 - University of Waterloo [Tutorial] [Post 1], [Post 2], [Post 3]
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CPSC 322: Introduction to Artificial Intelligence (UBC) [Tutorial] [Arabic Edition] [Post 1], [Post 2], [Post 3]
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Artificial Intelligence_ A Modern Approach, 4th Edition (2021) [Book] [Post 1], [Post 2]
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Machine Learning: A Probabilistic Perspective [Book] [Post 1], [Post 2],[Post 3] [Advanced]
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[First Check This Post] [Date:
June 2022]
- Artificial Intelligence By Example Acquire Advanced AI, Machine Learning and Deep Learning design skill [Book] [Post 1], [Post 2]
- Mastering Machine Learning Algorithms, Second Edition [Book] [Post 1], [Post 2]
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[First Check This Post] [Date:
January 2021]
- Mastering Machine Learning Algorithms, Second Edition [Book] [Post 1], [Post 2]
- Artificial Intelligence By Example Acquire Advanced AI, Machine Learning and Deep Learning design skill [Book] [Post 1], [Post 2]
- Artificial Intelligence_ A Modern Approach, 4th Edition (2021) [Book] [Post 1], [Post 2]
- CPSC 322: Introduction to Artificial Intelligence (UBC) [Tutorial] [Arabic Edition] [Post 1], [Post 2], [Post 3]
- Excel Data Analysis 2nd ed. 2019 Edition [Book] [Post link]
- EBooks (machinelearningmastery.com) [Post link]
- Artificial Intelligence,A Guide to Intelligent Systems (2001) [Book] [Post link]
- Artificial Intelligence_ A Modern Approach, 4th Edition (2021) [Book] [Post 1], [Post 2]
- Pattern Recognition and Machine Learning (2006) [Christopher Bishop] [Book] [Tutorial] [Post 1], [Post 2], [Post 3], [Post 4], [Post 5]
- Bayesian Reasoning and Machine Learning [Book] [Post link]
- Python Data Science Handbook [Book] [Post link]
- Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking [Book] [Post link]
- Machine Learning: A Probabilistic Perspective [Book] [Advanced] [Post 1], [Post 2],[Post 3]
- Bayesian Reasoning and Machine Learning [Book] [Advanced] [Post link]
- MITx 6.00.2x, Introduction to Computational Thinking and Data Science [Tutorial] [What is DATA Science ? - Entry Course] [Post link]
- Machine Learning Specialization - Andrew Ng [Specialization] [Post link]
- Vrije Universiteit Amsterdam [Tutorial] [Post 1] [Post 2]
- Deep Learning Drizzle [Roadmap] [Post link] [Very Important ๐ฅ]
- Pattern Recognition Class (2012) [Post link]
- Neural Networks and Deep Learning By Michael Nielsen [Book] [Beginner] [Post 1] [Post 2]
- Probabilistic Deep Learning with Python [Book] [Post link]
- Neural Networks and Statistical Learning [Book] [Post link]
- Evolutionary Approach to Machine Learning and Deep Neural Networks (Ch. 3)[Book] [Post link]
- Graph Neural Networks [Book] [Post link]
- Deep Learning Book by Aaron Courville, Ian Goodfellow [Book] [Post link]
- Deep Learning for the Life Sciences [Book] [Post link]
- NPTEL | Deep Learning- Part 1, IIT Ropar [Tutorial] [Post link]
- NPTEL | Deep Learning - Part 2, IIT Madras [Tutorial] [Post link]
- Neural networks class - Universitรฉ de Sherbrooke | Hugo Larochelle [Tutorial] [Post link]
- Neural Networks for Machine Learning by Geoffrey Hinton (Coursera 2013) [Tutorial] [Post link]
- Theoretical Foundations of Graph Neural Networks (GNNS) [Tutorial] [Post link]
- MIT Deep Learning in Life Sciences 6.874 Spring 2020 [Tutorial] Syllabus [Post link]
- MIT Deep Learning in Life Sciences (Spring 2021) [Tutorial] [Post link]
- CS 182: Deep Learning (Spring 2021) [Tutorial] Post Link
- Deep Learning With PyTorch - Full Course [Tutorial] Post Link
- Tรผbingen Machine Learning - [Post Link]
- Kapil Sachdeva - [Post link]
- Meerkat Statistics - [Post link]
- Machine Learning and AI Academy - [Post link]
- DeepFindr - [Post link]
- The AI Epiphany - [Post link]
- The Math Sorcerer
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ูุชูุงูู ูุงุณ ูุชูุฑ ุนุงูุฒู ุชุนู ู ูุงุฑููุฑ ุดููุช ุงูู AI.
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ุงูุช ููู ุฏุงูู ุง ุชููู ุงู Data Science ุนูู ุนู ูู ู ุชุงูุฏ ุงูู ุนูู ุฏุง ุ
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.ุนูุดุงู ุชุชุนูู ูููุณ ูุนูู ู ุญุชุงุฌ ุณูุชูู ู ุง ุจูู ูุธุฑู ู ุชุทุจูู
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ู ุชุญุงููุด ุชุชุนูู ู ู ู ุตุงุฏุฑ ุจุชูุชุจ ุงู Math ู ู ุบูุฑ Visualization ูุงุถุญุฉ.
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ุงูู ุณุงุฆู ุงููู AI ุจุชูุฑูุนุงุชู ุจูุญููุง ู ููุณู ุฉ ุงูู ููุนูู.
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ูู ุงุชุนูู ุช ุชูุฑุฃ Mathematical notations ูุชูู ู
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ูู ุงููู ุชุนุฑูู ู ู ุงูุณุงููุณ ูุงูู ู ูุง ู ุญุชุงุฌ ุชูู ู ู ุฐุงูุฑู ุ
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ูุจู ู ุง ุชุงุฎุฏ ูุฑุงุฑ ุงูู ุชูุชู ุจุงู Deep Learning.
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ุนูุดุงู ุชููู CNN,RNN ูุงุฒู ุชุชุนูู Neural Networks ูููุณ ุงูู ุงูู
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Generative Models ุงู ู ุญุงููุฉ ูููู ุดุบููู ู ู ุณูุฉ ุบูุฑ Probability ูุชูุดู.
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ูุฑุงุก ุงุจุญุงุซ ุงู Deep Learning ุงูู ุฎุชููุฉ ููุฑ ูุงุญุฏ.
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Transformer ูุญูู ููุฏ ู ู ูุบู ุจุฑู ุฌู ุงูู ุงุฎุฑู.
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ูุจู ุงู ู ุญุงููุฉ ูุชุนูู Probabilistic Graphical Models
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ูู ู ุณุชุฎุฏู ุชุด PyTorch ุญุชู ุงูุงู ุฌุฑุจู ู ู ุด ูุชูุฏู .
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ุงูุง ูุฏุนุช TensorFlow ูุตุงูุญ PyTorch ู ู ุญูุงูู ุณูุฉ.
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ุงุฐุง ููุช ู ูุชู ุจุงู Deep Learning ู ุนูุฏู ุนูู Graduate Knowledge ุฎููู ู ุนุงูุง
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Computational Graph (1), Computational Graph (2), Computational Graph (3)
- ุฏูุฑ ุนูู ุงูุฎุจุฑุฉ ุงููู ูุชุจูู ุนูููุง ู ุณุชูุจูู ุงูู ูููุ ู ุด ุนูู ุงุณู ููุง ูููุณ
- ูู ุจุฏุฃุช ูู ุญุงุฌุฉ ูู ููุง.
- Algorithms
- Deep Learning From Scratch I: Computational Graphs - sabinasz.net
This repository is nothing but a compilation of the great work done by Eng. Mohamed Hammad, and thanks to Eyad Hamza for this great idea. ;)