brandon.ai

What It Is

Build Thinking Machines is an online book/tutorial titled An introduction to artificial intelligence and machine learning algorithms, aimed at introducing artificial intelligence and machine learning algorithms. It is not a typical video course or bootcamp; instead, it explains machine learning models through chapter-based text and diagrams, with accompanying open-source code on GitHub to help learners deepen their understanding through implementation.

Core Content and Learning Approach

Based on the available content, the course covers AI/ML fundamentals, univariate and multivariate linear regression, polynomial regression, logistic regression, random forests, K-Means clustering, as well as linear algebra, probability, and statistics for machine learning. Its defining feature is a first-principles approach: for example, in the linear regression chapter, it starts by building intuition through stock price prediction, then gradually introduces the hypothesis function, cost function, gradient descent, and parameter optimization. The math explanations are designed to be “practical enough,” and the material states that a high-school-level math background is sufficient to follow along, with later chapters offering math refreshers.

Pricing, Certificates, and Instructors

The page does not show pricing, purchase options, certificates, or proof of completion, nor does it provide clear information about the author’s credentials or institutional background. As a result, it feels more like open learning material than a commercial course with certification, assignment grading, or career services. The teaching language is English, so Chinese learners will need a certain level of technical reading ability in English.

Pros and Cons

Its main strength is its very clear positioning: it is aimed at people with a software engineering background but limited machine learning experience, especially developers who want to understand the underlying mechanisms of mainstream machine learning libraries, frameworks, or cloud services. The accompanying open-source code is also helpful for hands-on model implementation. The limitations are that the available content does not indicate any live classes, recorded lectures, 1-on-1 support, Q&A, community, exercises, or assessment-based teaching support, so the learning path depends heavily on self-discipline. There is also no certificate information, making it less suitable for users whose main goal is to obtain a credential for job applications.

Who It’s For and Access from China

It is suitable for programmers, software engineers, self-learners starting out in algorithms, and anyone who wants to implement basic machine learning models from scratch. It is less suitable for learners with no programming background at all, those who need Chinese-language explanations, or those who require strong supervision and tutoring support. Access from China cannot be determined from the text alone; the accompanying GitHub code may be affected by local network conditions. If you need Chinese-language alternatives, you could consider machine learning courses on 中国大学MOOC, 学堂在线, or 网易云课堂; if English is acceptable, you may compare it with platforms such as Coursera, edX, and fast.ai.