{"id":27799064,"url":"https://github.com/tk-learning-center/machine-learning-degree","last_synced_at":"2025-05-01T00:01:59.623Z","repository":{"id":41843932,"uuid":"115825457","full_name":"imteekay/machine-learning-research","owner":"imteekay","description":"✨ ML/AI, Medicine, Genomics, Science Research","archived":false,"fork":false,"pushed_at":"2025-04-27T18:50:44.000Z","size":57470,"stargazers_count":176,"open_issues_count":0,"forks_count":29,"subscribers_count":6,"default_branch":"master","last_synced_at":"2025-04-27T19:36:21.881Z","etag":null,"topics":["data-science","deep-learning","machine-learning","python","science"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","readme":"\u003csamp\u003e\n\n# ML Research\n\n## Table of Contents\n\n- [ML Research](#ml-research)\n  - [Table of Contents](#table-of-contents)\n  - [Mathematics](#mathematics)\n    - [General Math](#general-math)\n    - [How to learn mathematics](#how-to-learn-mathematics)\n    - [Linear Algebra](#linear-algebra)\n    - [Statistics](#statistics)\n    - [Calculus](#calculus)\n    - [Optimization](#optimization)\n  - [Programming](#programming)\n    - [Python](#python)\n  - [Artificial Intelligence](#artificial-intelligence)\n    - [Learning Roadmap](#learning-roadmap)\n    - [Data Science Fundamentals](#data-science-fundamentals)\n    - [Machine Learning](#machine-learning)\n    - [Support Vector Machines](#support-vector-machines)\n    - [Advanced Machine Learning](#advanced-machine-learning)\n    - [Deep Learning](#deep-learning)\n    - [PyTorch](#pytorch)\n    - [Generative AI](#generative-ai)\n    - [Books](#books)\n    - [Podcasts](#podcasts)\n    - [Communities](#communities)\n    - [Online Courses](#online-courses)\n    - [Questions and Answers](#questions-and-answers)\n    - [Data Science Journey](#data-science-journey)\n    - [ML/AI \\\u0026 Healthcare](#mlai--healthcare)\n    - [ML/AI \\\u0026 Biology](#mlai--biology)\n    - [Databases](#databases)\n    - [Lists](#lists)\n  - [Science](#science)\n    - [Fundamentals](#fundamentals)\n    - [Science](#science-1)\n    - [Cancer](#cancer)\n    - [Genetics](#genetics)\n    - [Computational Biology](#computational-biology)\n    - [Precision Health](#precision-health)\n    - [Meta](#meta)\n    - [Central Resources](#central-resources)\n    - [Science: Q\\\u0026A](#science-qa)\n  - [Projects](#projects)\n  - [Interview Prep](#interview-prep)\n  - [Careers](#careers)\n  - [People](#people)\n  - [Research \\\u0026 Laboratories](#research--laboratories)\n  - [License](#license)\n\n## Mathematics\n\n### General Math\n\n- [Data Science Math Skills](https://www.coursera.org/learn/datasciencemathskills)\n- [Mathematics of Big Data and Machine Learning](https://ocw.mit.edu/courses/res-ll-005-mathematics-of-big-data-and-machine-learning-january-iap-2020)\n- [Mathematics for Machine Learning](https://github.com/imteekay/mathematics-for-machine-learning)\n- [How to get from high school math to cutting-edge ML/AI](https://www.justinmath.com/how-to-get-from-high-school-math-to-cutting-edge-ml-ai)\n- [The Complete Mathematics of Neural Networks and Deep Learning](https://www.youtube.com/watch?v=Ixl3nykKG9M)\n- [Math   Academy](https://www.mathacademy.com)\n\n### How to learn mathematics\n\n- [How to study math — Jo Boaler](https://www.youtube.com/watch?v=pRsutB2NhLk\u0026list=TLPQMjkwNzIwMjND3tvET8TH0g\u0026index=2\u0026ab_channel=LexClips)\n- [How To Self-Study Math](https://www.youtube.com/watch?v=fb_v5Bc8PSk\u0026list=TLPQMjkwNzIwMjND3tvET8TH0g\u0026index=3\u0026ab_channel=TheMathSorcerer)\n- [How to learn physics \u0026 math](https://www.youtube.com/watch?v=klEFaIZuiYk\u0026list=TLPQMjkwNzIwMjND3tvET8TH0g\u0026index=4\u0026ab_channel=Tibees)\n- [Best Way to Learn Math](https://www.youtube.com/watch?v=zvrleanEYOw\u0026list=TLPQMjkwNzIwMjND3tvET8TH0g\u0026index=5\u0026ab_channel=LexClips)\n- [How to learn math — Jordan Ellenberg](https://www.youtube.com/watch?v=UcpmwBOVp44\u0026list=TLPQMjkwNzIwMjND3tvET8TH0g\u0026index=6\u0026ab_channel=LexClips)\n- [Learn Mathematics from START to FINISH](https://www.youtube.com/watch?v=pTnEG_WGd2Q\u0026t=17s\u0026ab_channel=TheMathSorcerer)\n- [How to Learn Math](https://math.ucr.edu/home/baez/books.html#math)\n- [Why Learn Discrete Math?](https://www.youtube.com/watch?v=oJhAPsy9hBU\u0026ab_channel=Intermation)\n\n### Linear Algebra\n\n- [Linear Algebra at MIT](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures)\n- [Khan Academy Linear Algebra](https://www.khanacademy.org/math/linear-algebra)\n- [Linear algebra cheat sheet for deep learning](https://towardsdatascience.com/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c)\n- [[Course] Essence of linear algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)\n- [[Course] Linear Algebra Crash Course](https://www.youtube.com/watch?v=n9jZmymHX6o\u0026ab_channel=LunarTech)\n- [Linear Algebra Tutorial](https://www.youtube.com/watch?v=3Bf9oh7nkus\u0026ab_channel=metacodeM)\n- [Tiled Matrix Multiplication](https://penny-xu.github.io/blog/tiled-matrix-multiplication)\n- [The Big Picture of Linear Algebra](https://www.youtube.com/watch?v=ggWYkes-n6E)\n- [[Interview] Gilbert Strang: Linear Algebra](https://www.youtube.com/watch?v=lEZPfmGCEk0)\n- [Mathematics for Machine Learning - Linear Algebra](https://www.youtube.com/playlist?list=PLiiljHvN6z1_o1ztXTKWPrShrMrBLo5P3)\n- [Linear Algebra for Data Science](https://drive.google.com/file/d/1nJVwdQV9zp-Q9VQenZF0-HOOG6L2lEOD/view)\n- [Introduction to Linear Algebra for Applied Machine Learning with Python](https://pabloinsente.github.io/intro-linear-algebra)\n\n### Statistics\n\n- [Khan Academy Probability](https://www.khanacademy.org/math/probability)\n- [Khan Academy Statistics and probability](https://www.khanacademy.org/math/statistics-probability)\n- [Inferential Statistics](https://br.udacity.com/course/intro-to-inferential-statistics--ud201)\n- [Introduction to Statistics](https://www.coursera.org/learn/stanford-statistics)\n- [The better way to do statistics](https://www.youtube.com/watch?v=3jP4H0kjtng)\n- [A complete guide to box plots](https://www.atlassian.com/data/charts/box-plot-complete-guide)\n- [Probability and Statistics](https://www.youtube.com/playlist?list=PLMrJAkhIeNNR3sNYvfgiKgcStwuPSts9V)\n- [Probability for Computer Scientists](https://chrispiech.github.io/probabilityForComputerScientists/en)\n\n### Calculus\n\n- [[Course] Essence of calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr)\n- [Khan Academy Multivariable Calculus](https://www.khanacademy.org/math/multivariable-calculus)\n- [Khan Academy Differential Calculus](https://www.khanacademy.org/math/differential-calculus)\n- [Calculus Applied](https://www.edx.org/learn/calculus/harvard-university-calculus-applied)\n- [Mathematics for Machine Learning - Multivariate Calculus](https://www.youtube.com/playlist?list=PLiiljHvN6z193BBzS0Ln8NnqQmzimTW23)\n\n### Optimization\n\n- [Convex Optimization](https://web.stanford.edu/class/ee364a/videos.html)\n- [Understanding Gradient Descent](https://degatchi.com/articles/gradient-descent)\n\n## Programming\n\n### Python\n\n- [Practical Python Programming](https://dabeaz-course.github.io/practical-python/Notes/Contents.html)\n\n## Artificial Intelligence\n\n### Learning Roadmap\n\n- [Machine Learning Roadmap 2022](https://www.youtube.com/watch?v=y4o9hrSCDPI\u0026list=TLPQMzAxMjIwMjMIRqKttLLFsg\u0026index=3\u0026ab_channel=SmithaKolan-MachineLearningEngineer)\n- [How to learn AI and ML](https://www.youtube.com/watch?v=KEB-w9DUdCw\u0026ab_channel=PythonProgrammer)\n- [Recommendations by Ilya Sutskever](https://arc.net/folder/D0472A20-9C20-4D3F-B145-D2865C0A9FEE)\n\n### Data Science Fundamentals\n\n- [Fundamental Python Data Science Libraries: Numpy](https://hackernoon.com/fundamental-python-data-science-libraries-a-cheatsheet-part-1-4-58884e95c2bd)\n- [Fundamental Python Data Science Libraries: Pandas](https://hackernoon.com/fundamental-python-data-science-libraries-a-cheatsheet-part-2-4-fcf5fab9cdf1)\n- [Fundamental Python Data Science Libraries: Matplotlib](https://hackernoon.com/fundamental-python-data-science-libraries-a-cheatsheet-part-3-4-6c2aecc697a4)\n- [Fundamental Python Data Science Libraries: Scikit-Learn](https://hackernoon.com/fundamental-python-data-science-libraries-a-cheatsheet-part-4-4-fd8895ef85d5)\n- [Data Engineering Roadmap](https://github.com/hasbrain/data-engineer-roadmap)\n\n### Machine Learning\n\n- [Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning)\n- [Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning)\n- [Introduction to Machine Learning Course](https://www.udacity.com/course/intro-to-machine-learning--ud120)\n- [Learning Math for Machine Learning](https://blog.ycombinator.com/learning-math-for-machine-learning)\n- [Machine Learning at CMU](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml)\n- [Bishop Keynotes on ML](https://www.microsoft.com/en-us/research/people/cmbishop/#!videos)\n- [Machine Learning Guides by Google](https://developers.google.com/machine-learning/guides)\n- [Machine Learning Crash Course by Google](https://developers.google.com/machine-learning/crash-course/ml-intro)\n- [Facebook Field Guide to Machine Learning](https://research.fb.com/the-facebook-field-guide-to-machine-learning-video-series)\n- [Um pequeno guia para Data Science / Machine Learning](http://lgmoneda.github.io/2017/06/12/data-science-guide.html)\n- [Machine Learning for All](https://www.coursera.org/learn/uol-machine-learning-for-all)\n- [Reinforcement Learning](https://www.udacity.com/course/reinforcement-learning--ud600)\n- [Machine Learning Crash Course with TensorFlow APIs](https://developers.google.com/machine-learning/crash-course)\n- [Backpropagation from the ground up](https://www.youtube.com/watch?v=SmZmBKc7Lrs)\n- [Understanding Machine Learning: From Theory to Algorithms](https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf)\n- [CS229 Lecture Notes](https://cs229.stanford.edu/lectures-spring2022/main_notes.pdf)\n- [A theory-heavy intro to machine learning](https://0xpemulis.net/learningtheory.html)\n- [ML Code Challenges](https://www.deep-ml.com)\n- [Machine learning in Python with scikit-learn](https://lms.fun-mooc.fr/courses/course-v1:inria+41026+session03/6c7bd3e1d86545c4b723b844ae2702f9)\n- [Introduction to Algorithms and Machine Learning](https://www.justinmath.com/files/introduction-to-algorithms-and-machine-learning.pdf)\n- [How to actually learn AI/ML: Reading Research Papers](https://www.youtube.com/watch?v=x6slke5niqw)\n- [Machine Learning Fundamentals: Bias and Variance](https://www.youtube.com/watch?v=EuBBz3bI-aA)\n- [Machine Learning Fundamentals: Cross Validation](https://www.youtube.com/watch?v=fSytzGwwBVw)\n- [Machine Learning Specialization by Andrew Ng](https://www.youtube.com/playlist?list=PLkDaE6sCZn6FNC6YRfRQc_FbeQrF8BwGI)\n- [AI Fundamentals](https://www.udacity.com/course/ai-fundamentals--ud099)\n- [Artificial Intelligence](https://www.udacity.com/course/artificial-intelligence--ud954)\n- [📃 Hyper-Parameter Optimization: A Review of Algorithms and Applications](https://arxiv.org/pdf/2108.02497)\n- [📃 How to avoid machine learning pitfalls: a guide for academic researchers](https://arxiv.org/pdf/2108.02497)\n- [📃 Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://arxiv.org/pdf/1811.12808)\n- [Árvore de decisão](https://www.youtube.com/watch?v=W7MfsE5av0c)\n- [📃 How to avoid machine learning pitfalls: a guide for academic researchers](https://arxiv.org/pdf/2108.02497)\n- [Deep-ML](https://www.deep-ml.com)\n- [The ML Roadmap](https://github.com/loganthorneloe/ml-road-map)\n- [Why is machine learning 'hard'?](https://ai.stanford.edu/~zayd/why-is-machine-learning-hard.html)\n\n### Support Vector Machines\n\n- [Support Vector Machines Part 1 (of 3): Main Ideas](https://www.youtube.com/watch?v=efR1C6CvhmE)\n- [Support Vector Machines Part 2: The Polynomial Kernel](https://www.youtube.com/watch?v=Toet3EiSFcM)\n- [Support Vector Machines Part 3: The Radial (RBF) Kernel](https://www.youtube.com/watch?v=Qc5IyLW_hns)\n- [MIT — Learning: Support Vector Machines](https://www.youtube.com/watch?v=_PwhiWxHK8o)\n- [Support Vector Machines | Stanford CS229](https://www.youtube.com/watch?v=lDwow4aOrtg)\n\n### Advanced Machine Learning\n\n- [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book)\n- [A Reinforcement Learning Guide](https://naklecha.notion.site/a-reinforcement-learning-guide)\n\n### Deep Learning\n\n- [Intro to Deep Learning](https://www.kaggle.com/learn/intro-to-deep-learning)\n- [Deep Learning Book](http://www.deeplearningbook.org)\n- [fast.ai vs. deeplearning.ai](https://medium.com/@markryan_69718/learning-deep-learning-fast-ai-vs-deeplearning-ai-34f9c42cf701)\n- [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python)\n- [Dive into Deep Learning](https://d2l.ai/index.html)\n- [Intro to Deep Learning](http://introtodeeplearning.com/2020/index.html)\n- [Intro to Deep Learning with PyTorch](https://www.udacity.com/course/deep-learning-pytorch--ud188)\n- [Deep Learning Research and the Future of AI](https://www.youtube.com/watch?v=5BrNt38OraE\u0026ab_channel=MicrosoftResearch)\n- [[Paper] Sequence to Sequence Learning with Neural Networks](https://arxiv.org/pdf/1409.3215)\n- [Demystifying deep reinforcement learning](https://nail.cs.ut.ee/index.php/2015/12/19/globular-star-cluster-radio-scope-great-turbulent-clouds)\n- [A Review of: Human-Level Control through deep Reinforcement Learning](https://hci.iwr.uni-heidelberg.de/system/files/private/downloads/213797145/report_carsten_lueth_human_level_control.pdf)\n- [[Paper] Mastering the game of Go without human knowledge](https://www.nature.com/articles/nature24270.epdf?author_access_token=VJXbVjaSHxFoctQQ4p2k4tRgN0jAjWel9jnR3ZoTv0PVW4gB86EEpGqTRDtpIz-2rmo8-KG06gqVobU5NSCFeHILHcVFUeMsbvwS-lxjqQGg98faovwjxeTUgZAUMnRQ)\n- [AlphaGo Zero: Starting from scratch](https://deepmind.google/discover/blog/alphago-zero-starting-from-scratch)\n- [Neural Networks](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)\n- [The Principles of Deep Learning Theory](https://arxiv.org/pdf/2106.10165)\n- [Why do tree-based models still outperform deep learning on tabular data?](https://arxiv.org/pdf/2207.08815)\n- [MIT 6.S191: Introduction to Deep Learning](https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI)\n- [Deep Learning NYU](https://www.youtube.com/playlist?list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI)\n- [Building Neural Networks from Scratch](https://www.youtube.com/playlist?list=PLPTV0NXA_ZSj6tNyn_UadmUeU3Q3oR-hu)\n- [The Matrix Calculus You Need For Deep Learning](https://explained.ai/matrix-calculus)\n  - [Paper](https://arxiv.org/pdf/1802.01528)\n- [Convolution is Matrix Multiplication](https://penny-xu.github.io/blog/convolution-is-matrixmultiplication)\n- [Neural Networks and Deep Learning — Course 1](https://www.youtube.com/watch?v=CS4cs9xVecg\u0026list=PLkDaE6sCZn6Ec-XTbcX1uRg2_u4xOEky0)\n- [Improving Deep Neural Networks — Course 2](https://www.youtube.com/playlist?list=PLkDaE6sCZn6Hn0vK8co82zjQtt3T2Nkqc)\n- [Structuring Machine Learning Projects — Course 3](https://www.youtube.com/playlist?list=PLkDaE6sCZn6E7jZ9sN_xHwSHOdjUxUW_b)\n- [Convolutional Neural Networks — Course 4](https://www.youtube.com/playlist?list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF)\n- [Sequence Models — Course 5](https://www.youtube.com/playlist?list=PLkDaE6sCZn6F6wUI9tvS_Gw1vaFAx6rd6)\n- [Understanding Deep Learning Book Club](https://www.youtube.com/playlist?list=PLmp4AHm0u1g0AdLp-LPo5lCCf-3ZW_rNq)\n- [Dive into Deep Learning](https://d2l.ai)\n- [TABPFN: A transformer that solves small tabular classification problems in a second](https://arxiv.org/pdf/2207.01848)\n- [A Matemática das Redes Neurais](https://www.youtube.com/watch?v=qZ9xuPcoWSA)\n- [Introdução a Redes Neurais e Deep Learning](https://www.youtube.com/watch?v=Z2SGE3_2Grg)\n- [How do neural networks learn features from data?](https://www.youtube.com/watch?v=y0KxsLJvG14)\n- [Neural Networks: Zero to Hero](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)\n- [Building A Neural Network from Scratch with Mathematics and Python](https://www.iamtk.co/building-a-neural-network-from-scratch-with-mathematics-and-python)\n- [Neural Network from Scratch](https://github.com/imteekay/neural-network-from-scratch)\n- [Feedforward Neural Networks in Depth, Part 1: Forward and Backward Propagations](https://jonaslalin.com/2021/12/10/feedforward-neural-networks-part-1)\n- [Feedforward Neural Networks in Depth, Part 2: Activation Functions](https://jonaslalin.com/2021/12/21/feedforward-neural-networks-part-2)\n- [Feedforward Neural Networks in Depth, Part 3: Cost Functions](https://jonaslalin.com/2021/12/22/feedforward-neural-networks-part-3)\n- [[Paper] Three Decades of Activations: A comprehensive survey of 400 activation functions for neural networks](https://arxiv.org/pdf/2402.09092)\n- [A Gentle Introduction to Graph Neural Networks](https://distill.pub/2021/gnn-intro)\n- [Famous Deep Learning Papers](https://papers.baulab.info)\n- [[Paper] Decentralized Diffusion Models](https://arxiv.org/pdf/2501.05450)\n- [Deep Learning for Data Science (DL4DS)](https://dl4ds.github.io/sp2025/lectures)\n- [Recurrent Neural Networks cheatsheet](https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks)\n\n### PyTorch\n\n- [PyTorch internals](https://blog.ezyang.com/2019/05/pytorch-internals)\n\n### Generative AI\n\n- [Intuitions on Language Models \u0026 Shaping the Future of AI from the History of Transformer](https://www.youtube.com/watch?v=3gb-ZkVRemQ\u0026ab_channel=StanfordOnline)\n\n### Books\n\n- [The Elements of Statistical Learning](https://web.stanford.edu/~hastie/Papers/ESLII.pdf)\n- [Pattern Recognition and Machine Learning](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf)\n- [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow/dp/1787125939)\n- [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook)\n- [Think Stats: Exploratory Data Analysis in Python](http://greenteapress.com/thinkstats2/html/index.html)\n- [The Orange Book of Machine Learning](https://carl-mcbride-ellis.github.io/TOBoML/TOBoML.pdf)\n\n### Podcasts\n\n- [Data Science, Past, Present and Future with Hilary Mason](https://www.datacamp.com/community/podcast/data-science-past-present-and-future)\n\n### Communities\n\n- [Machine Learning Reddit](https://www.reddit.com/r/MachineLearning)\n- [NLP Reddit](https://www.reddit.com/r/LanguageTechnology)\n- [Statistics Reddit](https://www.reddit.com/r/statistics)\n- [Data Science Reddit](https://www.reddit.com/r/datascience)\n- [Machine Learning Quora Topic](https://www.quora.com/topic/Machine-Learning)\n- [Statistics Quora Topic](https://www.quora.com/topic/Statistics-academic-discipline)\n- [Data Science Quora Topic](https://www.quora.com/topic/Data-Science)\n- [Lee Lab of AI for bioMedical Sciences](https://suinlee.cs.washington.edu)\n- [Lab of big data and predictive analysis in healthcare](https://www.fsp.usp.br/labdaps)\n- [Jean Fan lab](https://jef.works)\n- [Pranav Rajpurkar](https://pranavrajpurkar.com)\n- [The AI Health Podcast](https://twitter.com/AIHealthPodcast)\n\n### Online Courses\n\n- [Preprocessing for Machine Learning in Python](https://www.datacamp.com/courses/preprocessing-for-machine-learning-in-python)\n- [Computer Science for Artificial Intelligence](https://www.edx.org/professional-certificate/harvardx-computer-science-for-artifical-intelligence)\n- [Machine Learning courses](https://www.edx.org/learn/machine-learning)\n- [Machine Learning Crash Course with TensorFlow APIs](https://developers.google.com/machine-learning/crash-course)\n- [Machine Learning Stanford Course](https://www.coursera.org/learn/machine-learning)\n- [Machine Learning with Python](https://www.coursera.org/learn/machine-learning-with-python)\n- [Math for Machine Learning with Python](https://www.edx.org/learn/math/edx-math-for-machine-learning-with-python)\n- [Machine Learning with Python: from Linear Models to Deep Learning](https://www.edx.org/learn/machine-learning/massachusetts-institute-of-technology-machine-learning-with-python-from-linear-models-to-deep-learning)\n\n### Questions and Answers\n\n- [Andrew Ng's answer on \"How should you start a career in Machine Learning?\"](https://www.quora.com/How-should-you-start-a-career-in-Machine-Learning)\n- [How do I learn mathematics for machine learning?](https://www.quora.com/How-do-I-learn-mathematics-for-machine-learning)\n- [How do I learn machine learning?](https://www.quora.com/How-do-I-learn-machine-learning-1)\n\n### Data Science Journey\n\n- [How to land a Data Scientist job at your dream company — My journey to Airbnb](https://towardsdatascience.com/how-to-land-a-data-scientist-job-at-your-dream-company-my-journey-to-airbnb-f6a1e99892e8)\n- [How to build a data science project from scratch](https://medium.freecodecamp.org/how-to-build-a-data-science-project-from-scratch-dc4f096a62a1)\n\n### ML/AI \u0026 Healthcare\n\n- [[Course] Machine Learning for Healthcare](https://www.edx.org/learn/machine-learning/massachusetts-institute-of-technology-machine-learning-for-healthcaregit)\n- [AI in Healthcare @ Google Brain](https://www.youtube.com/watch?v=cvXVK8oqU4Q\u0026ab_channel=AlexanderAmini)\n- [Healthcare's AI Future: A Conversation with Fei-Fei Li \u0026 Andrew Ng](https://www.youtube.com/watch?v=Gbnep6RJinQ\u0026ab_channel=StanfordHAI)\n- [AI and the Future of Health](https://www.microsoft.com/en-us/research/blog/ai-and-the-future-of-health)\n- [Aplicações de Deep Learning a Genética](https://www.youtube.com/watch?v=GiL6RnXLjvI)\n- [Daphne Koller: Biomedicine and Machine Learning](https://www.youtube.com/watch?v=xlMTWfkQqbY\u0026ab_channel=LexFridman)\n- [Data and resource needs for machine learning in genomics](https://www.youtube.com/watch?v=kjQ-8LFkeaA\u0026ab_channel=NationalHumanGenomeResearchInstitute)\n- [Machine Learning para Predições em Saúde](https://www.youtube.com/playlist?list=PLpvV74h3lihLdYrlnhlx_phy4pFZeZsKx)\n- [Inteligência Artificial em Saúde](https://www.youtube.com/playlist?list=PLAudUnJeNg4tvUFZ8tXQDoAkFAASQzOHm)\n- [[Course] Collaborative Data Science for Healthcare](https://www.edx.org/learn/data-science/massachusetts-institute-of-technology-collaborative-data-science-for-healthcare)\n- [[Course] Data Analytics and Visualization in Health Care](https://www.edx.org/learn/data-analysis/rochester-institute-of-technology-data-analytics-and-visualization-in-health-care)\n- [[Course] Introduction to Applied Biostatistics: Statistics for Medical Research](https://www.edx.org/learn/biostatistics/osaka-university-introduction-to-applied-biostatistics-statistics-for-medical-research)\n- [[Paper] Capabilities of Gemini Models in Medicine](https://arxiv.org/pdf/2404.18416)\n  - [Journal Club Debate: Capacidades dos modelos Gemini na medicina](https://www.youtube.com/watch?v=qj-4_dP6BQw)\n- [[Paper] Deep learning methods for drug response prediction in cancer: Predominant and emerging trends](https://www.frontiersin.org/articles/10.3389/fmed.2023.1086097/full)\n- [[Paper] Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties](https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0061318\u0026type=printable)\n- [[Paper] Artificial intelligence in healthcare: past, present and future](https://svn.bmj.com/content/svnbmj/2/4/230.full.pdf)\n- [Multimodal Generative AI: the Next Frontier in Precision Health](https://www.microsoft.com/en-us/research/quarterly-brief/mar-2024-brief/articles/multimodal-generative-ai-the-next-frontier-in-precision-health)\n- [Artificial Intelligence in Healthcare: Past, Present and Future](https://svn.bmj.com/content/svnbmj/2/4/230.full.pdf)\n- [[Paper] The myth of generalisability in clinical research and machine learning in health care](https://www.thelancet.com/action/showPdf?pii=S2589-7500%2820%2930186-2)\n- [[Paper] Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography](https://ascopubs.org/doi/pdfdirect/10.1200/JCO.22.01345)\n- [[Paper] Capabilities of Gemini Models in Medicine](https://arxiv.org/abs/2404.18416)\n- [[Paper] Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://arxiv.org/pdf/2311.16452)\n- [Large Language Models Encode Clinical Knowledge](https://arxiv.org/pdf/2212.13138)\n- [AI Aspirations Healthcare Futures](https://www.youtube.com/watch?v=Bn5M6hT3W1E)\n- [Breast Cancer Prediction: project](https://github.com/imteekay/breast-cancer-prediction)\n- [Training ML Models for Cancer Tumor Classification](https://www.iamtk.co/training-ml-models-for-cancer-tumor-classification)\n- [AI for Business Transformation: Lessons from Healthcare](https://www.youtube.com/watch?v=8C-XiXB67_Q)\n- [The revolution in high-throughput proteomics and AI](https://www.science.org/doi/10.1126/science.ads5749)\n- [[Course] AI for Medicine Specialization](https://www.deeplearning.ai/courses/ai-for-medicine-specialization)\n- [Towards Democratization of Subspeciality Medical Expertise](https://arxiv.org/pdf/2410.03741)\n- [Uncovering early predictors of cerebral palsy through the application of machine learning: a case-control study](https://bmjpaedsopen.bmj.com/content/bmjpo/8/1/e002800.full.pdf)\n- [Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk](https://watermark.silverchair.com/groos_2022_oi_220608_1656698661.11703.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAzEwggMtBgkqhkiG9w0BBwagggMeMIIDGgIBADCCAxMGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQM1jHKezBOqHeyGBSVAgEQgIIC5IKGlXjEvmbfHnrMFH3WqDX3nEMySJWaqxi9RQk-_fsW1yrXRseVGAYSDEElc6gPIbMpTmJ4hHCYzQhvIQ4igHIgJCq6U_8git_LEJR2GLAS3VE8HjBUsH0pqmhWDJ24P6WW94jgn9ZL3nPTrZX2nU6uL_ZFtDAo64muDnJ54N0NPgrxUQJOCyKz65neeKqVVM8mO6F60HFzbBuPapBMKVlTVpyB3UfDtVw7VuCPdFNyiQ8A7Z7EmzWR7rXL93pg2KztZ2-0qWqN8upA0XgN4N01LOFsanLZIf6TaZ5TTjpaAuWtgSrwurxJW5Wh3A2a6zr8SrfpGq92muV3XHJ4CtElyitZ1z9BKjZDkSrQSv9jpG8Of0ngOna4xCDwIMJ6CmaV9cajxs9ARCzmUlWyNxiVenwXCLR1z-x_W9QEeuTT58BUB9fRVStPKngy-7IG4IWbOaxAP8sLa50CtUkBPtOnichM0pdJWkuDYvOv_ylqDzoGjT6VVPk_wLVjJPGlisp9V0ZLea7gDI5OuHOfcDTO6rjWwynkUNAHZYM_dHCkBG0rFSlqxKarpOUMRR0Z6RqPJiAFzYGBnTBs2kpI0Ax5UD1Dhk2wxRcu7z8UALf-riLDXIzJZDXp_o8dHZW2HL5809Kt6k5OFiV5ovUenCXBLCDBhZC1I9r6bQD9M-CvDCBFP2vVNNUzIlT2ARGgluxXP_BOp3dQFSy3V5dwWR2vHhqj8_WFjn7kLPAiqNtjotcwYYPXrMg7mROH--dC3fwzZ608O5KXiZo717_1ftjNrWfQ-SYpq2nkkxIAln4NmoGsuIZgqHaTwZvmacMt-q0y6TQRSRkKVRhIWtF0XjVcjzlqfmOOwmF8ehdsmMnovU1pL_vDGqj2TSVMpkgG3oQ6dHR-6OAzAlvsNtV_QsFxiWUCof9MSXafhQGGWaCoyTvXwK2Iy7lEwYYzu1H0WvpywrJD1SNqa4gcgo-KHZOInjVj)\n- [What VCs Look for When Investing in Bio and Healthcare](https://www.youtube.com/watch?v=t1AHFTCj4yo)\n- [[Paper] Dermatologist-level classification of skin cancer with deep neural networks](papers/dermatologist-level-classification-of-skin-cancer-with-deep-neural-networks.pdf)\n- [[Paper] Deep learning for healthcare: review, opportunities and challenges](https://watermark.silverchair.com/bbx044.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAA0wwggNIBgkqhkiG9w0BBwagggM5MIIDNQIBADCCAy4GCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMQ2-7mTx2wplagxXZAgEQgIIC_zHCUwGjE8hQ82MCrBKVwsCS1q5zpR2eGYgCruIlBx6Uz8NFqhaNjJFvOcs7ayAZcfmC9tPi_kfMf5vF9o5jjs-lpvqwS87nhaYMeHXX2cGqSSoAZVC2YYOvjmBPbMdsVNy9yvpFpIikO6Qi4OIx0V_itE7QxGfojUTHKBebd2kt6aLN4bO73rGSX-I_Q9ElPT3v7sdrjTnfrSBAR5K5XfCGE2JwlXEOfcyxnboQoELcCALtFszLF9Xb8EDciu_qXIDEFunAPQwScasT1a5IGqhSVRolejeRZuLCTu2XxpBBLEwcsPkzwgQVpJifpG10TbWPFTQzIyPX_KQDUyR9e9VFnHMs-goG1vLnT-HZQKEP-aLTAnY6zDBhICGLLfx66JQR9DRVHZGzKRJ_p9j2FVjJ107l0Ru1Lk0WrWptBCz5p-g-luZfEndVTpuAMPf_r3wQxhJuCn4luYj_RtSOR3sM7MxsJzS_-JBQgqmjAwMDElFjVOok2r7lYU0M2xU3r_YhJCCBhxAd7s_PsNkPNj-j9QcrEw_jQ0RxHGlv-t3mpmStrIuBBBQBBLdTbJJgMN9I0S9TS6rSaHL0W2VmHVvYXckM8QcuEmpMHVqjuysYYcbgBTr8gIP4HE40VLBUIzFZNHEeOi0tL2TLdorAabXGAhcxysfl4_h-S0FNNQsGx3M56h22quLenkeixWmVH7GpXcnTNdEYH44Nt4U5Kq_PqeI5Hz53eo_hN9LgGOeLANC7Z4nNmYtNrGMrKEbMUERJJMZrEjgglcPd9fydFLKXhL_KBJ-ha1CkgQmxoOD0nkjLS4qdwjiOWNpheNzaNkJGzj8fIn-CK0U3C28APP4kWJRK3HCkJyHwWpMNJsQNPgbR94NhDykOGkzJJVR_k7QHNfVIOd_MunbPCCWi0kdxmIMzFlbCCnHUvz0KMNpLIsPZuHVmXPb5VJqdLdX9Sx56hYl37tKLGu3W-oWI89Ts1eK01T60gZ7ki3DKLf0Afc1BMZmy9hkCcAVt6oCZ)\n- [[Paper] Opportunities and obstacles for deep learning in biology and medicine](https://royalsocietypublishing.org/doi/10.1098/rsif.2017.0387)\n- [[Paper] Deep Learning in Medical Image Analysis](https://pmc.ncbi.nlm.nih.gov/articles/PMC7442218/pdf/nihms-1617552.pdf)\n- [[Paper] CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://arxiv.org/pdf/1711.05225)\n- [[Paper] Medical deep learning—A systematic meta-review](https://www.sciencedirect.com/science/article/pii/S0169260722002565?via%3Dihub)\n- [[Paper] Scalable and accurate deep learning with electronic health records](papers/scalable-and-accurate-deep-learning-with-electronic-health-records/paper.pdf)\n- [[Paper] Dermatologist–level classification of skin cancer with deep neural networks](https://pmc.ncbi.nlm.nih.gov/articles/PMC8382232/pdf/nihms-1724608.pdf)\n- [[Paper] Deep Learning in Medicine](papers/deep-learning-in-medicine/paper.pdf)\n- [[Paper] Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology](https://arxiv.org/pdf/2402.14252)\n- [[Paper] Collaboration between clinicians and vision–language models in radiology report generation](papers/collaboration-between-clinicians-and-vision–language-models-in-radiology-report-generation/paper.pdf)\n- [[Paper] ReXplain: Translating Radiology into Patient-Friendly Video Reports](https://arxiv.org/pdf/2410.00441)\n\n### ML/AI \u0026 Biology\n\n- [[Paper] Machine learning-aided generative molecular design](https://www.nature.com/articles/s42256-024-00843-5)\n- [Causal Inference for Computational Biology](https://summit.sfu.ca/_flysystem/fedora/2023-05/etd22427.pdf)\n- [Simulating 500 million years of evolution with a language model](https://evolutionaryscale-public.s3.us-east-2.amazonaws.com/research/esm3.pdf)\n- [Learning to Plan Chemical Syntheses](https://www.semanticscholar.org/reader/ef8ab2a0be51a0cd04c2c0f01adfae956a2a84af)\n- [Machine Learning for Genomics](https://www.youtube.com/playlist?list=PLypiXJdtIca6dEYlNoZJwBaz__CdsaoKJ)\n- [MIT Deep Learning in Life Sciences](https://www.youtube.com/playlist?list=PLypiXJdtIca5sxV7aE3-PS9fYX3vUdIOX)\n- [AI Text2Protein Breakthrough Tackles the Molecule Programming Challenge](https://medium.com/310-ai/mpm4-ai-text2protein-breakthrough-tackles-the-molecule-programming-challenge-870045a8c1ad)\n- [Genomic Language Models: Opportunities and Challenges](https://arxiv.org/pdf/2407.11435)\n- [Melodia: A Python Library for Protein Structure Analysis](https://watermark.silverchair.com/btae468.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAA4swggOHBgkqhkiG9w0BBwagggN4MIIDdAIBADCCA20GCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMVNfSCiowdD1a6WnjAgEQgIIDPsK_bI3A6IGF7cjZqL-1PehaqGZsY0AwhsIWIAc5Qa0rKYxHgeqnDIClLsf0Ey_I6ps6u545OlMuxBXd7yIO3xB0N0EMbsq5qYVSHqnuiqu2-LShlZxwk0ICGlLuJDR0ROgvGT837Lh72d2Eax_WuXzx6bkr9L2eUBifW8x4fULkCBqFtvhySkJwwvIIYd46Pi8bgM-XeQZI1DjwxN4KuHG15xkQpbdGvMmYpSGJvJefQTnY_YzF94F7zheUVj4s3JRYpKPtbxhG-6ba525xHNpMiFOy7gIbdn2X3JlH7LlQu6qE77E27t43nzyGujAvZEMl0Fir4TXs59Syp-c7Ss6MkCe1eh_VQtzdA3R00o7MHNy2fL_ES_Vkjdf1WcAB4nWQogaw_xZyOptjJxUJfLZyUYkEHpvfiSDx7f6Xr0F9w-gy-2tSemDG7Bp0xGjJJqA3oDZ8KZlR1hINXtCFG9qHIMy0425YFsGJY8nsTyZ2ULFlP2aeH1nnvUY_3O9r7KN_hKZhauxn5qkV5aSY1owVH9GDraYyRf-5JxpqVVAiovkzoqwa5YJXlMgflbK1S-004q_vtlNO2E9Wijy6qjiNUoot3QKybZogrumKSAuuvZwRRtAbvDdt7pZFyqxfEp6G7ofjR-MHNlinTq9rku2zu3znlFWI7j-nny465XasRL04KJXzjHXOAjpc0Ww4Ns-xnS24kVACj_ioBQ4XWSsHMUSdZfttGBWE4AL-64Ll7avyn9U64iEf9grCct3Hu1Dub8wMcwbXzjN7OPb3FLTlT8-zLTgWFmuMXpI7PV4wYRzt61APV3OCDfoq21XTr9Qn-nTaiNESDsClOvL49ZqYPTwFunCYkfR-jhgH06vc6wdB9XXV5jgIqdD5z1JXv8g4XJV3BTTj5SpGhomM9LkHgkDtZwzqMzJClbtkQArncyzLAKoX2kLx2_8t5u69rqCV6mSVDPwoeiJjVcl0uK8UmnCnk8MvHyN6odT-u_osm7aihSojxqKHBRJxdS3eB3gXq4qdNb8qVMGACMNpH4x_bp0qPvKGCOKJV0Lncer6H3HeLHVbrD6KPvWv5_g8JirNW5RDe5umOhuD1rFJ)\n- [Biomolecular Modeling and Design Resources](https://abeebyekeen.com/categories/resources)\n- [Understanding AlphaFold – Dame Janet Thornton](https://www.youtube.com/watch?v=lxgaILSZEbU)\n- [Leveraging Molecular ML + Property Prediction in Drug Design](https://www.youtube.com/watch?v=wisrT2_EYrA)\n- [Geometric Deep Learning for Protein Understanding](https://www.youtube.com/watch?v=h7Rifw0Nuv4)\n- [Polaris: Industry-Led Initiative to Critically Assess ML for Real-World Drug Discovery](https://www.youtube.com/watch?v=Tsz_T1WyufI)\n- [Efficiently Exploring Combinatorial Perturbations From High Dimensional Observation](https://www.youtube.com/watch?v=8ZjqsgsPV_0)\n- [Towards Rational Drug Design with AlphaFold 3](https://www.youtube.com/watch?v=AE35XCN5NuU)\n- [How AI and accelerated computing are transforming drug discovery](https://www.ft.com/partnercontent/nvidia/how-ai-and-accelerated-computing-are-transforming-drug-discovery.html)\n- [Review and discussion of AlphaFold3](https://www.youtube.com/watch?v=qjFgthkKxcA)\n- [Understanding \u0026 discovering fold-switching proteins by combining AlphaFold2](https://www.youtube.com/watch?v=rgGceDDnIEo)\n- [Accelerating drug discovery with AI](https://www.youtube.com/watch?v=-hl0jpwWbV4)\n- [Intro to ML in Drug Discovery: Principles \u0026 Applications](https://www.youtube.com/watch?v=j-oLfEm7xD8)\n- [Introduction to AI in Drug Discovery](https://www.youtube.com/watch?v=7NgPGh0E0XE)\n- [AlphaFold3: A foundation model for biology](https://harrisbio.substack.com/p/alphafold3-a-foundation-model-for)\n- [[Paper] Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7904235)\n- [[Paper] Deep learning in drug discovery: an integrative review and future challenges](https://github.com/imteekay/machine-learning-research/blob/master/papers/deep-learning-in-drug-discovery-an-integrative-review-and-future-challenges/paper.pdf)\n- [DeepMind AlphaFold 3](https://www.youtube.com/watch?v=Mz7Qp73lj9o\u0026ab_channel=TwoMinutePapers)\n- [[Course] Introduction to Genomic Data Science](https://www.edx.org/learn/bioinformatics/the-university-of-california-san-diego-introduction-to-genomic-data-science)\n- [Generative models for molecular discovery: Recent advances and challenges](https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.1608)\n- [Generative Models of Molecular Structures](https://www.youtube.com/watch?v=15bHUOjp6IU\u0026list=PLoVkjhDgBOt3NyXcTGg_fi-H8qBzNnKgk\u0026index=15)\n- [Opportunities and obstacles for deep learning in biology and medicine](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5938574/pdf/rsif20170387.pdf)\n- [Ten quick tips for machine learning in computational biology](https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0155-3)\n- [Machine learning and complex biological data](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1689-0)\n- [A guide to machine learning for biologists](https://hfenglab.org/NRev21.pdf)\n- [Next-Generation Machine Learning for Biological Networks](https://www.cell.com/action/showPdf?pii=S0092-8674%2818%2930592-0)\n- [AlphaFold3 — What’s next in computational drug discovery? — Part 1](https://medium.com/@leowossnig/alphafold3-whats-next-in-computational-drug-discovery-2da534c0845e)\n- [Deep generative models for biomolecular engineering](https://www.youtube.com/watch?v=4A51MwTuctk)\n- [Discovering New Molecules Using Graph Neural Networks](https://www.youtube.com/watch?v=fzSL7MWfXtQ)\n- [AI-Driven Drug Discovery Using Digital Biology](https://www.youtube.com/watch?v=27JMkAleyNw)\n- [Digital Biology with insitro's Daphne Koller](https://www.youtube.com/watch?v=79qJLY-30ao)\n- [AI-First: Daphne Koller’s plan to revolutionize drug discovery](https://www.youtube.com/watch?v=ukEaOOn9ZaE)\n- [AI for Medical Diagnosis](https://www.coursera.org/learn/ai-for-medical-diagnosis)\n- [AI for Medical Prognosis](https://www.coursera.org/learn/ai-for-medical-prognosis)\n- [AI For Medical Treatment](https://www.coursera.org/learn/ai-for-medical-treatment)\n- [[Paper] Generative models for molecular discovery: Recentadvances and challenges](https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.1608)\n- [How AI is saving billions of years of human research time](https://www.ted.com/talks/max_jaderberg_how_ai_is_saving_billions_of_years_of_human_research_time)\n\n### Databases\n\n- [Penn Machine Learning Benchmarks](https://epistasislab.github.io/pmlb)\n\n### Lists\n\n- [Papers on machine learning for proteins](https://github.com/yangkky/Machine-learning-for-proteins)\n- [Papers on Protein Design using Deep Learning](https://github.com/Peldom/papers_for_protein_design_using_DL)\n\n## Science\n\n### Fundamentals\n\n- [AP Biology](https://www.khanacademy.org/science/ap-biology)\n- [AP Chemistry](https://www.khanacademy.org/science/ap-chemistry-beta)\n- [Intro to Biology](https://www.khanacademy.org/science/biology)\n- [Intro to Chemistry](https://www.khanacademy.org/science/chemistry)\n- [Organic Chemistry](https://www.khanacademy.org/science/organic-chemistry)\n- [Introductory Biology](https://ocw.mit.edu/courses/7-016-introductory-biology-fall-2018)\n- [Molecular Biology - Part 1: DNA Replication and Repair](https://www.edx.org/learn/molecular-biology/massachusetts-institute-of-technology-molecular-biology-part-1-dna-replication-and-repair)\n- [Introduction to Biology - The Secret of Life](https://www.edx.org/learn/biology/massachusetts-institute-of-technology-introduction-to-biology-the-secret-of-life)\n\n### Science\n\n- [AI Case Studies for Natural Science Research](https://www.youtube.com/watch?v=rfPQ2y857eM\u0026ab_channel=MicrosoftResearch)\n- [How AI Is Unlocking the Secrets of Nature and the Universe](https://www.youtube.com/watch?v=0_M_syPuFos\u0026ab_channel=TED)\n- [Will AI Spark the Next Scientific Revolution?](https://www.youtube.com/watch?v=7wznuB0sKlw)\n\n### Cancer\n\n- [Introduction to the Biology of Cancer](https://www.coursera.org/learn/cancer)\n- [Understanding Prostate Cancer](https://www.coursera.org/learn/prostate-cancer)\n- [Understanding Cancer Metastasis](https://www.coursera.org/learn/cancer-metastasis)\n- [Ask a Researcher: Working in a Cancer Research Lab](https://www.youtube.com/watch?v=YJ8Fk6iLxdg\u0026list=TLPQMjUwNDIwMjIX07N_vVhBIQ\u0026index=5\u0026ab_channel=NationalCancerInstitute)\n- [What Causes Cancer?](https://www.youtube.com/watch?v=UlHK3Y_c5Wo\u0026ab_channel=UniversityofCaliforniaTelevision%28UCTV%29)\n- [What is Cancer?](https://www.youtube.com/watch?v=2X5kw3mVk08\u0026ab_channel=UniversityofCaliforniaTelevision%28UCTV%29)\n- [How is Cancer Diagnosed?](https://www.youtube.com/watch?v=oSOJbu5uqJE\u0026ab_channel=UniversityofCaliforniaTelevision%28UCTV%29)\n- [Cancer: Winning the War](https://www.youtube.com/playlist?list=PL504E935D23E00B4B)\n- [The Emperor of All Maladies: A Biography of Cancer](https://www.youtube.com/watch?v=D4BGYf2Nkks\u0026ab_channel=GBHForumNetwork)\n- [Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment](https://www.youtube.com/watch?v=x0-zGdlpTeg\u0026ab_channel=LexFridman)\n- [Tumour heterogeneity and resistance to cancer therapies](https://www.nature.com/articles/nrclinonc.2017.166)\n- [Porque mesmo com a ciência avançando tanto, ainda não temos uma cura para o câncer?](https://threadreaderapp.com/thread/1512474043290632194.html)\n\n### Genetics\n\n- [Cell Biology: Transport and Signaling](https://www.edx.org/learn/cellular-biology/massachusetts-institute-of-technology-cell-biology-transport-and-signaling)\n- [Introduction to Genomic Technologies](https://www.coursera.org/learn/introduction-genomics)\n- [Classical papers in molecular genetics](https://www.coursera.org/learn/papers-molecular-genetics)\n- [Genetics: The Fundamentals](https://www.edx.org/learn/genetics/massachusetts-institute-of-technology-genetics-the-fundamentals)\n- [[Course] Genetics: The Fundamentals](https://www.edx.org/learn/genetics/massachusetts-institute-of-technology-genetics-the-fundamentals)\n- [[Course] Genetics: Analysis and Applications](https://www.edx.org/learn/genetics/massachusetts-institute-of-technology-genetics-analysis-and-applications)\n- [[Course] Genomic Medicine Gets Personal](https://www.edx.org/learn/bioinformatics/georgetown-university-genomic-medicine-gets-personal)\n- [[Course] Essentials of Genomics and Biomedical Informatics](https://www.edx.org/learn/biomedical-sciences/israelx-essentials-of-genomics-and-biomedical-informatics)\n- [Genomics Papers](https://github.com/jtleek/genomicspapers)\n- [Jennifer Doudna: The Exciting Future of Genome Editing](https://www.youtube.com/watch?v=D4FOtJoqoKM)\n\n### Computational Biology\n\n- [Foundations of Computational and Systems Biology](https://ocw.mit.edu/courses/7-91j-foundations-of-computational-and-systems-biology-spring-2014)\n- [Bioinformatics](https://seen-politician-a47.notion.site/ccd895cfaee94849bc9c405a4143b4f5?v=8ca8b89a8be54d7c800a1dfe9780abfc)\n- [Understanding life via computational bioinformatics](https://www.youtube.com/watch?v=KH_ZxNu9vj4\u0026ab_channel=OrangeCountyACMChapter)\n\n### Precision Health\n\n- [Defining precision health: a scoping review protocol](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7888329/pdf/bmjopen-2020-044663.pdf)\n\n### Meta\n\n- [Fei-Fei Li \u0026 Demis Hassabis: Using AI to Accelerate Scientific Discovery](https://www.youtube.com/watch?v=KHFmIknP_Hc\u0026ab_channel=StanfordHAI)\n- [Science is the great giver](https://www.gatesnotes.com/European-Innovation)\n- [The Age of AI has begun](https://www.gatesnotes.com/The-Age-of-AI-Has-Begun)\n- [Writing in the Sciences](https://www.coursera.org/learn/sciwrite)\n- [How to read and understand a scientific paper: a guide for non-scientists](https://blogs.lse.ac.uk/impactofsocialsciences/2016/05/09/how-to-read-and-understand-a-scientific-paper-a-guide-for-non-scientists)\n- [Demis Hassabis, AI to Accelerate Scientific Discovery](https://www.youtube.com/watch?v=u1dl_keFK4w\u0026ab_channel=Axial)\n- [Demis Hassabis, AI for Science](https://www.youtube.com/watch?v=Q2JmdyqLqiw\u0026ab_channel=Axial)\n\n### Central Resources\n\n- [Armando Hasudungan](https://www.youtube.com/user/armandohasudungan)\n- [John Gilmore M.D.](https://www.youtube.com/channel/UCqBho4rDGlST_PY5I2Bh9yQ)\n- [Dr. Najeeb Lectures](https://www.youtube.com/channel/UCPHpx55tgrbm8FrYYCflAHw)\n- [MedCram - Medical Lectures Explained CLEARLY](https://www.youtube.com/channel/UCG-iSMVtWbbwDDXgXXypARQ)\n- [nabil ebraheim](https://www.youtube.com/user/nabilebraheim)\n- [Strong Medicine](https://www.youtube.com/channel/UCFq5vPnNRNNNysLrktz4aSw)\n- [Cancer Research Demystified](https://www.youtube.com/c/CancerResearchDemystified/featured)\n- [Cancer.Net](https://www.cancer.net)\n- [Books on Computational Molecular Biology](https://mitpress.mit.edu/books/series/computational-molecular-biology)\n- [Obenauf Lab](https://www.obenauflab.com)\n\n### Science: Q\u0026A\n\n- [As a computer science graduate student, I am motivated to do cancer research. How significantly can computer scientists contribute to cancer research? Where are such research institutes where I can pursue a PhD?](https://www.quora.com/As-a-computer-science-graduate-student-I-am-motivated-to-do-cancer-research-How-significantly-can-computer-scientists-contribute-to-cancer-research-Where-are-such-research-institutes-where-I-can-pursue-a-PhD)\n- [How can I contribute to cancer research as a computer engineering student if I have basic knowledge in artificial Intelligence?](https://www.quora.com/How-can-I-contribute-to-cancer-research-as-a-computer-engineering-student-if-I-have-basic-knowledge-in-artificial-Intelligence)\n- [What kind of knowledge gaps in molecular biology make cancer a big problem for researchers?](https://www.quora.com/What-kind-of-knowledge-gaps-in-molecular-biology-make-cancer-a-big-problem-for-researchers)\n\n## Projects\n\n- [Breast Cancer Prediction: Predicting whether breast cancer tumors are malignant or benign](https://github.com/imteekay/breast-cancer-prediction)\n\n## Interview Prep\n\n- [How I Prepared for DeepMind and Google AI Research Internship Interviews in 2019](https://davidstutz.de/how-i-prepared-for-deepmind-and-google-ai-research-internship-interviews-in-2019)\n\n## Careers\n\n- [ML Researcher at Borealis AI](careers/ml-researcher-borealis-ai.pdf)\n- [Crushing your interviews for Data Science and Machine Learning Engineering roles](https://building.nubank.com.br/crushing-your-interviews-for-data-science-and-machine-learning-engineering-roles-8-practical-tips)\n- [Research Scientist, Health AI — OpenAI](careers/research-scientist-health-ai-openaI.pdf)\n\n## People\n\n- [Andrej Karpathy](https://karpathy.ai)\n- [Alex Krizhevsky](https://www.cs.toronto.edu/~kriz)\n- [Geoffrey E. Hinton](https://www.cs.toronto.edu/~hinton)\n- [Rob Tibshirani](https://tibshirani.su.domains)\n- [Trevor Hastie](https://hastie.su.domains)\n- [Daniela Witten](https://www.danielawitten.com)\n- [Hattie Zhou](http://hattiezhou.com)\n- [Chelsea Voss](https://csvoss.com)\n- [Lillian](https://lilianweng.github.io)\n- [Christopher Olah](https://colah.github.io)\n- [Alex Irpan](https://www.alexirpan.com)\n- [Gwern Branwen](https://gwern.net)\n- [Jonathan Taylor](https://jtaylor.su.domains)\n- [Apoorva Srinivasan](https://www.apoorva-srinivasan.com)\n- [Susan Zhang](https://suchenzang.github.io)\n- [Michael Chang](https://mbchang.github.io)\n- [Jan Leike](https://jan.leike.name)\n- [Xiao Ma](https://maxiao.info)\n- [Gabriele Corso](https://gcorso.github.io)\n- [Falk Hoffmann](https://medium.com/@falk_hoffmann)\n- [Sara Hooker](https://www.sarahooker.me)\n- [Mario Geiger](https://mariogeiger.ch)\n- [Charlotte Bunne](https://www.bunne.ch)\n- [Charlie Harris](https://cch1999.github.io)\n- [Yuanqi Du](https://yuanqidu.github.io)\n- [Sophia Sanborn](https://www.sophiasanborn.com)\n- [Omar Sanseviero](https://osanseviero.github.io/hackerllama/blog)\n- [Simon Willison](https://simonwillison.net)\n- [Hamel Husain](https://hamel.dev)\n- [Philipp Schmid](https://www.philschmid.de)\n- [Eugene Yan](https://eugeneyan.com/writing)\n- [Chip Huyen](https://huyenchip.com/blog)\n- [Chenru Duan](https://www.crduan.com)\n- [Jeff Guo](https://guojeff.github.io)\n- [Arian Jamal](https://jamasb.io)\n- [Joseph Suárez](https://jsuarez5341.github.io)\n- [Andrew Ng](http://andrewng.org)\n- [Mathematics behind Deep learning](https://mathblog.vercel.app)\n- [Kevin Kaichuang Yang](https://yangkky.github.io)\n- [Terence Parr](https://explained.ai)\n- [Penny Xu](https://penny-xu.github.io)\n- [Amy X. Lu](https://amyxlu.github.io)\n- [Benjamin Bloem-Reddy](https://www.stat.ubc.ca/~benbr)\n- [Quanhan (Johnny) Xi](https://xijohnny.github.io)\n- [Eric Horvitz](https://erichorvitz.com)\n- [Rinaldo Montalvão](https://www.linkedin.com/in/rwmontalvao)\n- [Joanne Peng](https://www.joannepeng.com)\n- [Sarah Alamdari](https://www.sarahalamdari.com)\n- [Lorin Crawford](https://www.lorincrawford.com)\n- [Ava Amini](https://avaamini.com)\n- [Alex Lu](https://www.alexluresearch.com)\n- [Kevin Kaichuang Yang](https://yangkky.github.io)\n- [Rocío Mercado Oropeza](https://rociomer.github.io)\n- [Pranav Rajpurkar](https://pranavrajpurkar.com)\n- [Martin Steinegger](https://steineggerlab.com)\n- [Jue Wang](https://juewang.mystrikingly.com)\n- [Wenhu Chen](https://wenhuchen.github.io)\n- [Melanie Mitchell](https://melaniemitchell.me)\n- [Jenny Zhang](https://www.jennyzhangzt.com)\n- [Abhinav Gupta](https://www.guabhinav.com)\n- [Beidi Chen](https://www.andrew.cmu.edu/user/beidic)\n- [Avantika Lal](https://avantikalal.github.io)\n- [Will Connell](https://wconnell.github.io)\n- [Danqi Chen](https://www.cs.princeton.edu/~danqic)\n- [Yuqing Du](https://yuqingd.github.io)\n- [Apoorva Srinivasan](https://www.apoorva-srinivasan.com)\n- [Patrick Hsu](https://patrickhsu.com)\n- [Brian Hie](https://brianhie.com)\n- [Andrew Y. K. Foong](https://andrewfoongyk.github.io)\n- [Karina Nguyen](https://karinanguyen.com)\n- [Myle Ott](https://myleott.com)\n- [Reza Rezvan](https://rezarezvan.com)\n- [Artem Moskalev](https://amoskalev.github.io)\n- [Hamel](https://hamel.dev)\n- [Dr. Yejin Kim](https://yejinjkim.github.io)\n- [Jay Alammar](https://jalammar.github.io)\n- [Luis Serrano](https://serrano.academy)\n- [Jiayuan Mao](https://jiayuanm.com)\n- [Edward Z. Yang](https://ezyang.com)\n- [Edward Z. Yang's blog](https://blog.ezyang.com)\n\n## Research \u0026 Laboratories\n\n- [Microsoft Health Futures](https://www.microsoft.com/en-us/research/lab/microsoft-health-futures)\n\n## License\n\n[MIT](/LICENSE) © [TK](https://iamtk.co)\n\n\u003c/samp\u003e\n","funding_links":["https://github.com/sponsors/imteekay","https://teekay.substack.com"],"categories":["Degrees"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftk-learning-center%2Fmachine-learning-degree","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftk-learning-center%2Fmachine-learning-degree","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftk-learning-center%2Fmachine-learning-degree/lists"}