This article provides an overview of machine learning techniques for biologists, focusing on both traditional and deep learning methods. It discusses the importance of machine learning in biology due to the increasing complexity and scale of biological data. The article explains key concepts such as supervised and unsupervised learning, classification, regression, and clustering. It also covers the challenges of applying machine learning to biological data, including data leakage and the need for interpretability. The text outlines the use of various machine learning models, including traditional methods like support vector machines and random forests, as well as deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph convolutional networks (GCNs). It emphasizes the importance of proper model training, validation, and testing to avoid overfitting and ensure generalization. The article also discusses the role of attention mechanisms and transformer models in improving performance on biological sequences. Finally, it highlights the challenges of applying machine learning to biological data, including data availability, data leakage, and the need for interpretable models that can provide insights into biological processes.This article provides an overview of machine learning techniques for biologists, focusing on both traditional and deep learning methods. It discusses the importance of machine learning in biology due to the increasing complexity and scale of biological data. The article explains key concepts such as supervised and unsupervised learning, classification, regression, and clustering. It also covers the challenges of applying machine learning to biological data, including data leakage and the need for interpretability. The text outlines the use of various machine learning models, including traditional methods like support vector machines and random forests, as well as deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph convolutional networks (GCNs). It emphasizes the importance of proper model training, validation, and testing to avoid overfitting and ensure generalization. The article also discusses the role of attention mechanisms and transformer models in improving performance on biological sequences. Finally, it highlights the challenges of applying machine learning to biological data, including data availability, data leakage, and the need for interpretable models that can provide insights into biological processes.