AI/ML for Biology and Healthcare: A Learning Path (www.iamtk.co)

The author launched a living, comprehensive learning path aimed at preparing researchers and engineers to apply AI/ML in biology and healthcare. The roadmap stitches together foundational programming and software engineering, data engineering, traditional ML, deep learning, and domain knowledge in biology/healthcare, plus ML engineering/MLOps—emphasizing a practical, project-first approach (theory + notebooks + Kaggle-style exercises). It’s significant because it targets the cross-disciplinary gap: translating biological problems into data problems, optimizing data pipelines, and deploying robust ML systems—skills that are critical but often underrepresented in typical ML curricula. Technically, the guide prescribes mastery of programming basics, algorithms/data structures, git/CI-CD, and one DL framework (PyTorch/TensorFlow/JAX), then advances through Pandas/NumPy, EDA, data cleaning, feature engineering, and model evaluation. It recommends hands-on resources (learnpytorch.io, Deep Learning for Biology notebooks, Hands-On ML with Scikit-Learn/Keras/TensorFlow, Coursera specializations, ML in Production, Designing Machine Learning Systems) and covers specific models and techniques: supervised/unsupervised models (LR, SVM, trees, ensembles), neural networks, CNNs, RNNs/LSTMs, transformers, and advanced tools relevant to biology—LLMs for sequences, GNNs for structural data, VAEs/GANs/diffusion models—plus reinforcement learning and causal inference. The emphasis on data optimization, domain translation, and MLOps makes this a practical blueprint for anyone moving into biomedical ML.