<ul><li>Choosing the right algorithm for supervised learning tasks depends on the scale and dimensionality of the data, with neural networks, tree-based methods, and SVM being popular choices.</li><li>For image data, convolutional neural networks are often preferred, while boosted trees tend to perform well with tabular data having many features.</li><li>In unsupervised learning, clustering, dimensionality reduction, and association rule mining are commonly used, with the choice depending on the goal.</li><li>Testing multiple algorithms and iteratively improving the approach is key to finding the optimal solution, being flexible, and creative in applying algorithms.</li></ul>

Navigating the ML Maze: Choosing the Right Algorithm for the Right Problem

Discover more