Machine Learning cơ bản is more like a structured self-study blog for machine learning than a standard online course platform. The crawled content shows that the site organizes a large number of machine learning articles by number, covering topics such as linear regression, K-means, KNN, gradient descent, logistic regression, feature engineering, SVM, PCA, recommendation systems, Naive Bayes, Decision Trees, Keras, 2D convolution, and a brief history of deep learning. The site also offers a forum, comment sections, a Facebook page/group, recommended books and course links, and mentions that an ebook of the same name has been completed.
The content focuses on the fundamentals of machine learning and data science, with an emphasis on gradually building from algorithmic concepts to the underlying mathematics. Taking the “Feature Engineering” article as an example, it explains concepts such as the training/testing workflow, feature extraction, Bag-of-Words, dimensionality reduction, and standardization, with examples from computer vision, NLP, MNIST, and more. The learning format is mainly self-study through text-and-image blog posts. There is no sign of live classes, recorded video lessons, 1-on-1 tutoring, assignment grading, or formal cohort-based classes. The teaching language is Vietnamese, which is friendly for Vietnamese-speaking learners but creates a relatively high barrier for Chinese users.
The main content does not show any subscription fees or course charges. The blog articles appear to be freely readable. The author mentions that readers can support the site via “Buy me a coffee” and also says that the ebook can be ordered, but no pricing or payment methods are disclosed. As for certification, there is no information about completion certificates, exams, credits, or professional credentials, so it is not suitable for learners whose main goal is obtaining a certificate.
The strengths are its broad coverage, clear progression through the foundations of traditional machine learning, and access to a forum and community channels for questions. The explanations are fairly detailed, making it suitable as a long-term self-study resource. The drawbacks are that it is not a highly interactive course and lacks video demonstrations, project assignments, learning progress management, and a certificate system. Some articles were published around 2017–2018, so content related to Keras and deep learning frameworks may need to be supplemented with more up-to-date materials.
It is best suited to self-learners with some background in mathematics and programming who want to systematically strengthen their understanding of machine learning concepts in Vietnamese. It can also serve as a review resource for algorithms. The main content does not make it possible to determine access status from mainland China, and payment information is also unclear. If access or language is an issue, alternatives include Andrew Ng Machine Learning, Stanford CS231n/CS224n, fast.ai, 李宏毅机器学习课程, or Datawhale open-source materials.
⚠ This review is compiled from public sources and does not constitute a purchase recommendation. Verify all facts on the vendor's official site. Verify on machinelearningcoban.com official site.
machinelearningcoban.com is an Vietnam Education provider. TG4G tracks its product information, an overall rating of 6.0/10, and a China-accessibility score of Workable. Click "Visit Official Site" to reach machinelearningcoban.com directly.