- ML Research
- Data Science Math Skills
- Mathematics of Big Data and Machine Learning
- Mathematics for Machine Learning
- How to get from high school math to cutting-edge ML/AI
- The Complete Mathematics of Neural Networks and Deep Learning
- How to study math — Jo Boaler
- How To Self-Study Math
- How to learn physics & math
- Best Way to Learn Math
- How to learn math — Jordan Ellenberg
- Learn Mathematics from START to FINISH
- How to Learn Math
- Why Learn Discrete Math?
- Linear Algebra at MIT
- Khan Academy Linear Algebra
- Linear algebra cheat sheet for deep learning
- [Course] Essence of linear algebra
- [Course] Linear Algebra Crash Course
- Linear Algebra Tutorial
- Tiled Matrix Multiplication
- The Big Picture of Linear Algebra
- [Interview] Gilbert Strang: Linear Algebra
- Mathematics for Machine Learning - Linear Algebra
- Linear Algebra for Data Science
- Khan Academy Probability
- Khan Academy Statistics and probability
- Inferential Statistics
- Introduction to Statistics
- The better way to do statistics
- A complete guide to box plots
- Probability and Statistics
- Probability for Computer Scientists
- [Course] Essence of calculus
- Khan Academy Multivariable Calculus
- Khan Academy Differential Calculus
- Calculus Applied
- Mathematics for Machine Learning - Multivariate Calculus
- Fundamental Python Data Science Libraries: Numpy
- Fundamental Python Data Science Libraries: Pandas
- Fundamental Python Data Science Libraries: Matplotlib
- Fundamental Python Data Science Libraries: Scikit-Learn
- Data Engineering Roadmap
- Intro to Machine Learning
- Intermediate Machine Learning
- Introduction to Machine Learning Course
- Learning Math for Machine Learning
- Machine Learning at CMU
- Bishop Keynotes on ML
- Machine Learning Guides by Google
- Machine Learning Crash Course by Google
- Facebook Field Guide to Machine Learning
- Um pequeno guia para Data Science / Machine Learning
- Machine Learning for All
- Reinforcement Learning
- Machine Learning Crash Course with TensorFlow APIs
- Backpropagation from the ground up
- Understanding Machine Learning: From Theory to Algorithms
- CS229 Lecture Notes
- A theory-heavy intro to machine learning
- ML Code Challenges
- Machine learning in Python with scikit-learn
- Introduction to Algorithms and Machine Learning
- How to actually learn AI/ML: Reading Research Papers
- Machine Learning Fundamentals: Bias and Variance
- Machine Learning Fundamentals: Cross Validation
- Machine Learning Specialization by Andrew Ng
- AI Fundamentals
- Artificial Intelligence
- 📃 Hyper-Parameter Optimization: A Review of Algorithms and Applications
- 📃 How to avoid machine learning pitfalls: a guide for academic researchers
- 📃 Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning
- Árvore de decisão
- 📃 How to avoid machine learning pitfalls: a guide for academic researchers
- Support Vector Machines Part 1 (of 3): Main Ideas
- Support Vector Machines Part 2: The Polynomial Kernel
- Support Vector Machines Part 3: The Radial (RBF) Kernel
- MIT — Learning: Support Vector Machines
- Support Vector Machines | Stanford CS229
- Intro to Deep Learning
- Deep Learning Book
- fast.ai vs. deeplearning.ai
- Deep Learning with Python
- Dive into Deep Learning
- Intro to Deep Learning
- Intro to Deep Learning with PyTorch
- Deep Learning Research and the Future of AI
- [Paper] Sequence to Sequence Learning with Neural Networks
- Demystifying deep reinforcement learning
- A Review of: Human-Level Control through deep Reinforcement Learning
- [Paper] Mastering the game of Go without human knowledge
- AlphaGo Zero: Starting from scratch
- Neural Networks
- The Principles of Deep Learning Theory
- Why do tree-based models still outperform deep learning on tabular data?
- MIT 6.S191: Introduction to Deep Learning
- Deep Learning NYU
- Building Neural Networks from Scratch
- The Matrix Calculus You Need For Deep Learning
- Convolution is Matrix Multiplication
- Neural Networks and Deep Learning — Course 1
- Improving Deep Neural Networks — Course 2
- Structuring Machine Learning Projects — Course 3
- Convolutional Neural Networks — Course 4
- Sequence Models — Course 5
- Understanding Deep Learning Book Club
- Dive into Deep Learning
- TABPFN: A transformer that solves small tabular classification problems in a second
- A Matemática das Redes Neurais
- Introdução a Redes Neurais e Deep Learning
- How do neural networks learn features from data?
- Neural Networks: Zero to Hero
- Building A Neural Network from Scratch with Mathematics and Python
- Neural Network from Scratch
- Feedforward Neural Networks in Depth, Part 1: Forward and Backward Propagations
- Feedforward Neural Networks in Depth, Part 2: Activation Functions
- Feedforward Neural Networks in Depth, Part 3: Cost Functions
- The Elements of Statistical Learning
- Pattern Recognition and Machine Learning
- Python Machine Learning
- Python Data Science Handbook
- Think Stats: Exploratory Data Analysis in Python
- The Orange Book of Machine Learning
- Machine Learning Reddit
- NLP Reddit
- Statistics Reddit
- Data Science Reddit
- Machine Learning Quora Topic
- Statistics Quora Topic
- Data Science Quora Topic
- Lee Lab of AI for bioMedical Sciences
- Lab of big data and predictive analysis in healthcare
- Jean Fan lab
- Pranav Rajpurkar
- The AI Health Podcast
- Preprocessing for Machine Learning in Python
- Computer Science for Artificial Intelligence
- Machine Learning courses
- Machine Learning Crash Course with TensorFlow APIs
- Machine Learning Stanford Course
- Machine Learning with Python
- Math for Machine Learning with Python
- Machine Learning with Python: from Linear Models to Deep Learning
- Andrew Ng's answer on "How should you start a career in Machine Learning?"
- How do I learn mathematics for machine learning?
- How do I learn machine learning?
- How to land a Data Scientist job at your dream company — My journey to Airbnb
- How to build a data science project from scratch
- [Course] Machine Learning for Healthcare
- AI in Healthcare @ Google Brain
- Healthcare's AI Future: A Conversation with Fei-Fei Li & Andrew Ng
- AI and the Future of Health
- Aplicações de Deep Learning a Genética
- Daphne Koller: Biomedicine and Machine Learning
- Data and resource needs for machine learning in genomics
- Machine Learning para Predições em Saúde
- Inteligência Artificial em Saúde
- [Course] Collaborative Data Science for Healthcare
- [Course] Data Analytics and Visualization in Health Care
- [Course] Introduction to Applied Biostatistics: Statistics for Medical Research
- [Paper] Capabilities of Gemini Models in Medicine
- [Paper] Deep learning methods for drug response prediction in cancer: Predominant and emerging trends
- [Paper] Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties
- [Paper] Artificial intelligence in healthcare: past, present and future
- Multimodal Generative AI: the Next Frontier in Precision Health
- Artificial Intelligence in Healthcare: Past, Present and Future
- [Paper] The myth of generalisability in clinical research and machine learning in health care
- [Paper] Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography
- [Paper] Capabilities of Gemini Models in Medicine
- [Paper] Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
- Large Language Models Encode Clinical Knowledge
- AI Aspirations Healthcare Futures
- Breast Cancer Prediction: project
- Training ML Models for Cancer Tumor Classification
- AI for Business Transformation: Lessons from Healthcare
- The revolution in high-throughput proteomics and AI
- [Course] AI for Medicine Specialization
- Towards Democratization of Subspeciality Medical Expertise
- Uncovering early predictors of cerebral palsy through the application of machine learning: a case-control study
- Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk
- What VCs Look for When Investing in Bio and Healthcare
- [Paper] Dermatologist-level classification of skin cancer with deep neural networks
- [Paper] Deep learning for healthcare: review, opportunities and challenges
- [Paper] Opportunities and obstacles for deep learning in biology and medicine
- [Paper] Deep Learning in Medical Image Analysis
- [Paper] CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
- [Paper] Medical deep learning—A systematic meta-review
- [Paper] Scalable and accurate deep learning with electronic health records
- [Paper] Dermatologist–level classification of skin cancer with deep neural networks
- [Paper] Deep Learning in Medicine
- [Paper] Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology
- [Paper] Collaboration between clinicians and vision–language models in radiology report generation
- [Paper] Machine learning-aided generative molecular design
- Causal Inference for Computational Biology
- Simulating 500 million years of evolution with a language model
- Learning to Plan Chemical Syntheses
- Machine Learning for Genomics
- MIT Deep Learning in Life Sciences
- AI Text2Protein Breakthrough Tackles the Molecule Programming Challenge
- Genomic Language Models: Opportunities and Challenges
- Melodia: A Python Library for Protein Structure Analysis
- Biomolecular Modeling and Design Resources
- Understanding AlphaFold – Dame Janet Thornton
- Leveraging Molecular ML + Property Prediction in Drug Design
- Geometric Deep Learning for Protein Understanding
- Polaris: Industry-Led Initiative to Critically Assess ML for Real-World Drug Discovery
- Efficiently Exploring Combinatorial Perturbations From High Dimensional Observation
- Towards Rational Drug Design with AlphaFold 3
- How AI and accelerated computing are transforming drug discovery
- Review and discussion of AlphaFold3
- Understanding & discovering fold-switching proteins by combining AlphaFold2
- Accelerating drug discovery with AI
- Intro to ML in Drug Discovery: Principles & Applications
- Introduction to AI in Drug Discovery
- AlphaFold3: A foundation model for biology
- [Paper] Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images
- [Paper] Deep learning in drug discovery: an integrative review and future challenges
- DeepMind AlphaFold 3
- [Course] Introduction to Genomic Data Science
- Generative models for molecular discovery: Recent advances and challenges
- Generative Models of Molecular Structures
- Opportunities and obstacles for deep learning in biology and medicine
- Ten quick tips for machine learning in computational biology
- Machine learning and complex biological data
- A guide to machine learning for biologists
- Next-Generation Machine Learning for Biological Networks
- AlphaFold3 — What’s next in computational drug discovery? — Part 1
- Deep generative models for biomolecular engineering
- Discovering New Molecules Using Graph Neural Networks
- AI-Driven Drug Discovery Using Digital Biology
- Digital Biology with insitro's Daphne Koller
- AI-First: Daphne Koller’s plan to revolutionize drug discovery
- AI for Medical Diagnosis
- AI for Medical Prognosis
- AI For Medical Treatment
- [Paper] Generative models for molecular discovery: Recentadvances and challenges
- How AI is saving billions of years of human research time
- AP Biology
- AP Chemistry
- Intro to Biology
- Intro to Chemistry
- Organic Chemistry
- Introductory Biology
- Molecular Biology - Part 1: DNA Replication and Repair
- Introduction to Biology - The Secret of Life
- AI Case Studies for Natural Science Research
- How AI Is Unlocking the Secrets of Nature and the Universe
- Will AI Spark the Next Scientific Revolution?
- Introduction to the Biology of Cancer
- Understanding Prostate Cancer
- Understanding Cancer Metastasis
- Ask a Researcher: Working in a Cancer Research Lab
- What Causes Cancer?
- What is Cancer?
- How is Cancer Diagnosed?
- Cancer: Winning the War
- The Emperor of All Maladies: A Biography of Cancer
- Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment
- Tumour heterogeneity and resistance to cancer therapies
- Porque mesmo com a ciência avançando tanto, ainda não temos uma cura para o câncer?
- Cell Biology: Transport and Signaling
- Introduction to Genomic Technologies
- Classical papers in molecular genetics
- Genetics: The Fundamentals
- [Course] Genetics: The Fundamentals
- [Course] Genetics: Analysis and Applications
- [Course] Genomic Medicine Gets Personal
- [Course] Essentials of Genomics and Biomedical Informatics
- Genomics Papers
- Jennifer Doudna: The Exciting Future of Genome Editing
- Foundations of Computational and Systems Biology
- Bioinformatics
- Understanding life via computational bioinformatics
- Fei-Fei Li & Demis Hassabis: Using AI to Accelerate Scientific Discovery
- Science is the great giver
- The Age of AI has begun
- Writing in the Sciences
- How to read and understand a scientific paper: a guide for non-scientists
- Demis Hassabis, AI to Accelerate Scientific Discovery
- Demis Hassabis, AI for Science
- Armando Hasudungan
- John Gilmore M.D.
- Dr. Najeeb Lectures
- MedCram - Medical Lectures Explained CLEARLY
- nabil ebraheim
- Strong Medicine
- Cancer Research Demystified
- Cancer.Net
- Books on Computational Molecular Biology
- Obenauf Lab
- 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?
- How can I contribute to cancer research as a computer engineering student if I have basic knowledge in artificial Intelligence?
- What kind of knowledge gaps in molecular biology make cancer a big problem for researchers?
- ML Researcher at Borealis AI
- Crushing your interviews for Data Science and Machine Learning Engineering roles
- Research Scientist, Health AI — OpenAI
- Andrej Karpathy
- Alex Krizhevsky
- Geoffrey E. Hinton
- Rob Tibshirani
- Trevor Hastie
- Daniela Witten
- Hattie Zhou
- Chelsea Voss
- Lillian
- Christopher Olah
- Alex Irpan
- Gwern Branwen
- Jonathan Taylor
- Apoorva Srinivasan
- Susan Zhang
- Michael Chang
- Jan Leike
- Xiao Ma
- Gabriele Corso
- Falk Hoffmann
- Sara Hooker
- Mario Geiger
- Charlotte Bunne
- Charlie Harris
- Yuanqi Du
- Sophia Sanborn
- Omar Sanseviero
- Simon Willison
- Hamel Husain
- Philipp Schmid
- Eugene Yan
- Chip Huyen
- Chenru Duan
- Jeff Guo
- Arian Jamal
- Joseph Suárez
- Andrew Ng
- Mathematics behind Deep learning
- Kevin Kaichuang Yang
- Terence Parr
- Penny Xu
- Amy X. Lu
- Benjamin Bloem-Reddy
- Quanhan (Johnny) Xi
- Eric Horvitz
- Rinaldo Montalvão
- Joanne Peng
- Sarah Alamdari
- Lorin Crawford
- Ava Amini
- Alex Lu
- Kevin Kaichuang Yang
- Rocío Mercado Oropeza
- Pranav Rajpurkar
- Martin Steinegger
- Jue Wang
- Wenhu Chen
- Melanie Mitchell
- Jenny Zhang
- Abhinav Gupta
- Beidi Chen
- Avantika Lal