Foundational ML Courses

• Machine Learning Specialization by Andrew Ng (Coursera) – The best introduction to ML fundamentals. Start here if you’re new to machine learning.
• Fast.ai Practical Deep Learning for Coders – Top-down approach to learning deep learning. Free, practical, and code-first.
• Stanford CS229: Machine Learning – Comprehensive course materials and lecture notes available free online.
• MIT 6.S191: Introduction to Deep Learning – Excellent lectures covering modern deep learning with accessible explanations.

Biology-Specific ML Resources

• Deep Learning for the Life Sciences by Ramsundar et al. – Specifically designed for applying deep learning to biological problems. Code examples included.
• Machine Learning for Bioinformatics (UW-Madison) – Course materials covering ML applications in genomics and proteomics.
• Molecular Machine Learning – Interactive textbook for applying ML to molecular and chemical data.
• ML for Healthcare (MIT) – Focuses on clinical and health data applications.

Books and Textbooks

• Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron – Practical ML implementation guide with clear examples.
• Pattern Recognition and Machine Learning by Christopher Bishop – Comprehensive mathematical treatment. Free PDF available.
• Deep Learning by Goodfellow, Bengio, and Courville – The definitive deep learning textbook. Free online.
• An Introduction to Statistical Learning (ISLR) – Essential bridge between statistics and ML with R code.

Python Libraries and Tools

• Scikit-learn Documentation – The go-to library for classical ML in Python. Excellent tutorials and examples.
• PyTorch Tutorials – Modern deep learning framework with great documentation.
• TensorFlow and Keras Guides – Comprehensive guides for building neural networks.
• Biopython ML Tools – Python tools specifically for computational biology.

Specialized Topics

• StatQuest Neural Networks Playlist – Josh Starmer’s clear explanations of neural networks and deep learning concepts.
• 3Blue1Brown Neural Networks Series – Beautiful visual intuition for how neural networks work.
• Genomic Deep Learning – Tools and tutorials for applying deep learning to genomic sequences.
• AlphaFold Resources – Learn from DeepMind’s protein structure prediction breakthrough.

Practical Notebooks and Tutorials

• Google Colab ML Tutorials – Free GPU access and pre-made notebooks for ML experimentation.
• Kaggle Learn – Interactive ML courses with hands-on coding exercises.
• Papers with Code – Research papers with implementation code, great for staying current.
• Hugging Face Course – Modern NLP and transformer models, applicable to biological sequences.

Communities and Updates

• r/bioinformatics – Active community discussing ML applications in biology.
• Towards Data Science – Medium publication with practical ML tutorials and case studies.
• distill.pub – Beautiful, interactive explanations of ML concepts.

Additional resources

You might be interested in…

• Top ML and AI resources
• Useful resources for bioinformatics
• Top resources to learn biostatistics
• Learn to code as a biologist