10 ML models thread

A July 2026 social post distilled machine learning into a working toolkit: learn the models teams still reach for when the data and deadlines are real. (x.com) Machine learning is software that finds patterns in examples and uses them to predict, sort, or group new data. Google’s Machine Learning Crash Course teaches regression, classification, neural networks, and production concerns as the core path for applied work. (developers.google.com) The list starts with linear regression and logistic regression, two baseline models that estimate a number or a probability from input features. Google’s course presents linear regression for continuous targets and logistic regression for predicting the probability of an outcome. (developers.google.com) Decision trees come next: they split data into if-then branches, which makes them easy to inspect and explain. Google’s decision-forest course says a single tree is the building block for larger tree ensembles and can handle numerical, categorical, and missing features with less preprocessing. (developers.google.com) Random forest is the “many trees” version of that idea, where multiple trees vote instead of one tree deciding alone. Google describes random forests as ensembles of decision trees trained with randomness, a design that often improves accuracy over a single tree at the cost of more training and inference time. (developers.google.com) Support vector machines and k-nearest neighbors are older workhorses that still show up in tabular and small-data problems. Scikit-learn’s user guide lists support vector machines under supervised learning and describes nearest neighbors as methods for both classification and regression. (scikit-learn.org) Naive Bayes is the fast classifier in the set: it makes strong independence assumptions, but it trains quickly and often works well as a baseline. Scikit-learn groups Gaussian, Multinomial, Bernoulli, Complement, and Categorical Naive Bayes under one family in its supervised-learning guide. (scikit-learn.org) K-means and principal component analysis cover the unsupervised side, where the data arrive without labels. Scikit-learn defines K-means as a clustering algorithm that forms a chosen number of clusters, and PCA as a dimensionality-reduction method that projects data into a lower-dimensional space. (scikit-learn.org 1) (scikit-learn.org 2) Neural networks round out the list, but even Google’s beginner course places them after regression and classification basics. Its current modules introduce perceptrons, hidden layers, and activation functions before moving to larger systems such as large language models. (developers.google.com) The throughline in the post matches how mainstream tooling is organized in 2026: scikit-learn still centers classification, regression, clustering, dimensionality reduction, and model selection, while Google’s courses separate “advanced” architectures from the first-pass models people deploy every day. (scikit-learn.org) (developers.google.com) That is why the list reads less like a syllabus than a field manual: start with the simple model that fits the task, measure it, and only move up the complexity ladder when the data justify it. (x.com)