A gentle introduction to the procedures to learn models from data for 10 popular and useful supervised machine learning algorithms used for predictive modeling. Each algorithm includes one or more step-by-step tutorials explaining exactly how to plug in numbers into each equation and what numbers to expect as output. Each tutorial is designed to be completed in a spreadsheet.
Jason Brownlee, Ph.D. trained and worked as a research scientist and software engineer for many years (e.g. enterprise, R&D, and scientific computing), and is known online for his work on Computational Intelligence (e.g. Clever Algorithms), Machine Learning and Deep Learning (e.g. Machine Learning Mastery, sold in 2021) and Python Concurrency (e.g. Super Fast Python).
A clear and concise introduction to basic machine learning algorithms. It opens with a conceptual overview of ML basics and then walks the reader through the most commonly used algorithms, including examples with toy datasets that illustrate what they're actually doing. You won't get much out of the book if you're already relatively familiar with ML, though. It's oriented for people with minimal exposure to ML, statistics, applied mathematics, etc., and the technical content is much too sparse to gain a decent understanding of ML broadly or the particular algorithms. You would definitely need to consult more detailed texts to use the algorithms in a serious applied setting. There's also no code, so you will need to pick that up elsewhere.