Скачать или смотреть Machine Learning for Biologists; Lecture 1 (Python Fundamental)

In this video series, we introduce and explore machine learning techniques tailored to the needs and challenges of biological research. The goal is to equip biologists (and computationally curious life scientists) with the conceptual understanding and practical skills to apply machine learning to real biological data.

You will learn:

The fundamental principles of machine learning (supervised, unsupervised, model validation, overfitting, feature selection)

How to preprocess biological data, including normalization, handling missing values, dimensionality reduction

Key algorithms frequently used in biology: classification (e.g. logistic regression, random forests), clustering, dimensionality reduction (PCA, t-SNE, UMAP), neural networks / deep learning

How to build predictive models (e.g. gene expression prediction, disease classification) and assess their performance

Special considerations in biological datasets: high dimensionality, sparsity, batch effects, class imbalance, interpretability, bias & confounding

Case studies and applications in genomics, proteomics, metagenomics, single-cell RNA-seq, image-based phenotyping, etc.

Best practices: cross-validation, regularization, model explainability, feature importance

Tools and frameworks (e.g. Python libraries like scikit-learn, TensorFlow / PyTorch, bioinformatics toolkits) with code examples

Emerging directions: graph-based models, representation learning, transfer learning, integrative multi-omics modeling

By the end of this playlist, you should be able to:

Understand which machine learning methods are appropriate for different types of biological data.

Preprocess, clean, and transform real biological datasets for ML.

Build, train, and evaluate machine learning models in a reproducible way.

Interpret model outputs in a biologically meaningful way, assessing confidence and pitfalls.

Explore more advanced ML / AI approaches and adapt them to your own research questions.
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