└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Machine-Learning-Curriculum 📚 2 | One question that has frequently come my way lately is, “_How can one embark on their AI journey_?” A few years ago, answering this question would have been straightforward. However, with the recent rapid advancements spanning various AI domains, particularly in NLP and computer vision, and the abundance of online resources available today, outlining a comprehensive roadmap has become a nuanced task. 3 | 4 | After careful consideration, I’ve crafted a straightforward yet potent A-to-Z curriculum. This guide is designed to be inclusive, catering to individuals with diverse backgrounds — whether they are newcomers to the field or professionals seeking a career shift. These resources can also be beneficial for advanced ML engineers who want to refresh some concepts. 5 | 6 | [Medium Blog](http://tinyurl.com/3rykdnfj) 7 | 8 | # Structure 🧩 9 | The structure of this guide is deliberate and includes a personally curated list of online courses. Since this repository is beginner-friendly, I only included important structured courses in the main curriculum because they are organized and easier to follow. Additional resources will be added in separate sections. Take the main curriculum courses in order for better understanding. 10 | 11 | # Contributions 🚀 12 | This repository is open to all kinds of contributions related to the **machine learning** journey. However, there are some considerations: 13 | - Since the purpose here is to make a straightforward guide, additional courses, and books won't be added to the main curriculum. Rather they will be added in a separate section. 14 | - If a course meets standards, it will become a part of the main curriculum. 15 | - Resources can be free and paid. 16 | - Make sure to follow the format for resources i.e. name, links, and institute/person. 17 | - Resources can include YouTube channels, papers, blog posts, online courses, and book recommendations. 18 | 19 | ## How to contribute: 20 | - Fork this repository 21 | - Add your contribution (do not modify the original list) 22 | - Create Pull Request 23 | 24 | # Note 📝 25 | - There aren't many resources related to MLOps included in this repository since I am planning to create a separate repository for that. 26 | - *Practical resources and projects coming soon.* 27 | 28 | # Some Tips 🔎 29 | - AI learning is a journey, not a sprint; success requires resisting impatience, embracing challenges, and fostering a deep understanding of AI principles despite the temptation for quick results. 30 | - Broaden your machine learning understanding through diverse resources like instructors, courses, books, research papers, and blogs for a well-rounded grasp of artificial intelligence. 31 | - Focus on mastering one concept at a time for a solid foundation and effective learning. 32 | - Theory is vital, but true understanding comes from hands-on implementation; actively engage with knowledge, invest time in real-world problem-solving, and trust the process for profound insights. 33 | - AI success requires technical skills and more—embrace GitHub, Docker, diverse programming languages, paper-reading, cloud computing, project management, and strong writing/documentation for adaptability in the evolving industry. 34 | 35 | # Main Curriculum 📌 36 | ## Foundations 37 | [AI for Everyone by Coursera](https://www.coursera.org/learn/ai-for-everyone) 38 | 39 | [Mathematics for Machine Learning and Data Science Specialization by Coursera](https://www.coursera.org/specializations/mathematics-for-machine-learning-and-data-science) 40 | 41 | [CS50’s Introduction to Computer Science](https://www.edx.org/learn/computer-science/harvard-university-cs50-s-introduction-to-computer-science) 42 | 43 | [Complete Python Developer: Zero to Mastery](https://zerotomastery.io/courses/learn-python/) (*You can often find it on Udemy on sale.*) 44 | 45 | ## Machine Learning 46 | [Machine Learning Specialization by DeepLearningAI x Stanford](https://www.coursera.org/specializations/machine-learning-introduction) 47 | 48 | [Introduction to Machine Learning by Sebastian Raschka](https://sebastianraschka.com/blog/2021/ml-course.html) 49 | 50 | ## Deep Learning 51 | [CS231N: Convolutional Neural Networks for Visual Recognition by Stanford](https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk) 52 | 53 | [Introduction to Deep Learning by Sebastian Raschka](https://sebastianraschka.com/blog/2021/dl-course.html) 54 | 55 | [CS224N: Natural Language Processing with Deep Learning by Stanford](https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ) 56 | 57 | [Full Stack Deep Learning](https://fullstackdeeplearning.com/course/2022/) 58 | 59 | # Reference Books 📂 60 | [Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka , Yuxi (Hayden) Liu , Vahid Mirjalili](https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312) 61 | 62 | [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition by Aurélien Géron](https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/) 63 | 64 | [Mathematics for Machine Learning](https://mml-book.github.io/) 65 | 66 | --- 67 | 68 | # Additional Resources 🗞️ 69 | ## Courses 70 | [MIT Linear Algebra](https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/) 71 | 72 | [Statistics and Probability by Khan Academy](https://www.khanacademy.org/math/statistics-probability) 73 | 74 | [Essense of Linear Algebra by 3Blue1Brown](https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab&pp=iAQB) 75 | 76 | [Neural Networks: Zero to Hero by Andrej Karpathy](https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ&ab_channel=AndrejKarpathy) 77 | 78 | [Deep Learning Specialization by DeepLearningAI](https://www.coursera.org/specializations/deep-learning) 79 | 80 | [MIT 6.S191: Introduction to Deep Learning](https://www.youtube.com/watch?v=QDX-1M5Nj7s&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) 81 | 82 | [Deep Learning Fundamentals — Learning Deep Learning With a Modern Open Source Stack](https://lightning.ai/courses/deep-learning-fundamentals/) 83 | 84 | [Practical Deep Learning for Coders](https://course.fast.ai/) 85 | 86 | [Introduction to Reinforcement Learning by Deepmind](https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ&ab_channel=GoogleDeepMind) 87 | 88 | [CS50’s Introduction to Artificial Intelligence with Python](https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python) 89 | 90 | [AI for Beginners by Microsoft](https://microsoft.github.io/AI-For-Beginners/) 91 | 92 | [A detailed list of courses by Aman Chadha](https://aman.ai/watch/) 93 | 94 | [Machine Learning Crash Course by Google](https://developers.google.com/machine-learning/crash-course/ml-intro) 95 | 96 | [Dive into Deep Learning](https://d2l.ai/) 97 | 98 | [Applied Machine Learning by Cornell University](https://kuleshov-group.github.io/aml-book/intro.html) 99 | 100 | ## Books 101 | [Deep learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville](https://www.deeplearningbook.org/) 102 | 103 | [The Hundred-Page Machine Learning Book by Andriy Burkov](https://themlbook.com/) 104 | 105 | ## Youtube Channels 106 | [3Blue1Brown](https://www.youtube.com/@3blue1brown) 107 | 108 | [StatQuest with Josh Starmer](https://www.youtube.com/@statquest) 109 | 110 | [Andrej Karpathy](https://www.youtube.com/@AndrejKarpathy) 111 | 112 | [Lightning AI](https://www.youtube.com/@PyTorchLightning) 113 | 114 | [IBM Technology](https://www.youtube.com/@IBMTechnology) 115 | 116 | [Sentdex](https://www.youtube.com/@sentdex) 117 | 118 | [Aleksa Gordić - The AI Epiphany](https://www.youtube.com/@TheAIEpiphany) 119 | 120 | [Yannic Kilcher](https://www.youtube.com/@YannicKilcher) 121 | 122 | [Connor Shorten](https://www.youtube.com/@connorshorten6311) 123 | 124 | [Computerphile](https://www.youtube.com/@Computerphile) 125 | 126 | ## Blogs 127 | [Google AI Blog](https://ai.googleblog.com/) 128 | 129 | [Meta AI Blog](https://ai.facebook.com/blog/?page=1) 130 | 131 | [OpenAI](https://openai.com/blog/) 132 | 133 | [ML CMU](https://blog.ml.cmu.edu/) 134 | 135 | [Distill](https://distill.pub/) 136 | 137 | [BAIR Berkeley](https://bair.berkeley.edu/blog/) 138 | 139 | [DeepMind](https://deepmind.com/) 140 | 141 | [MIT ML](https://news.mit.edu/topic/machine-learning?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=machine-learning-blogs-to-follow) 142 | 143 | [Facebook AI](https://ai.facebook.com/) 144 | 145 | [Amazon AI](https://aws.amazon.com/blogs/machine-learning/?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=machine-learning-blogs-to-follow) 146 | 147 | [Stanford AI Lab Blog](https://ai.stanford.edu/blog/) 148 | 149 | [Chip Huyen Blog](https://huyenchip.com/blog/) 150 | 151 | [philschmid](https://www.philschmid.de/) 152 | 153 | [Lightning AI Blog](https://lightning.ai/blog/) 154 | 155 | ## Newsletters 156 | 157 | [Ahead of AI](https://magazine.sebastianraschka.com/) 158 | 159 | [The Batch](https://www.deeplearning.ai/the-batch/) 160 | 161 | [Import AI](https://jack-clark.net/) 162 | 163 | [Alphasignal](https://alphasignal.ai/) 164 | 165 | [The Ai Edge](https://newsletter.theaiedge.io/) 166 | 167 | ## Tutorials 168 | [NumPy tutorial by Stanford CS231N](https://cs231n.github.io/python-numpy-tutorial/) 169 | 170 | [PyTorch Tutorials](https://pytorch.org/tutorials/) 171 | 172 | [IBM Deep Learning Tutorials](https://developer.ibm.com/technologies/deep-learning/tutorials/) 173 | 174 | [Keras Code Examples](https://keras.io/examples/) 175 | 176 | [Huggingface Notebooks](https://huggingface.co/docs/transformers/notebooks) 177 | 178 | [UvA Deep Learning Tutorials](https://uvadlc-notebooks.readthedocs.io/en/latest/index.html) 179 | 180 | ## Projects 181 | Visit my [website](https://pytholic-homepage.vercel.app/works) for some project ideas. 182 | 183 | ## Papers 184 | *Coming soon.* 185 | 186 | ## Gold Blog Posts 187 | ### Beginner 188 | ["Yes you should understand backprop" by Andrej Karpathy](https://karpathy.medium.com/yes-you-should-understand-backprop-e2f06eab496b) 189 | 190 | ["A Recipe for Training Neural Networks" by Andrej Karpathy](http://karpathy.github.io/2019/04/25/recipe/) 191 | 192 | ### Intermediate 193 | ["The Unreasonable Effectiveness of Recurrent Neural Networks" by Andrej Karpathy](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) 194 | 195 | ["Understanding LSTM Networks" by Chris Olah](https://colah.github.io/posts/2015-08-Understanding-LSTMs/) 196 | 197 | ["The Illustrated Transformer" by Jay Alammar](http://jalammar.github.io/illustrated-transformer/) 198 | 199 | ["Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch" by Sebastian Raschka](https://sebastianraschka.com/blog/2023/self-attention-from-scratch.html) 200 | 201 | ### Advanced 202 | 203 | ["Illustrating Reinforcement Learning from Human Feedback (RLHF)" by HuggingFace](https://huggingface.co/blog/rlhf) 204 | 205 | ["RLHF: Reinforcement Learning from Human Feedback" by Chip Huyen](https://huyenchip.com/2023/05/02/rlhf.html) 206 | 207 | ["The Illustrated Stable Diffusion" by Jay Alammar](https://jalammar.github.io/illustrated-stable-diffusion/) 208 | --------------------------------------------------------------------------------