Section 1.1: mlcompendium

The mlcompendium project is an open-source initiative led by Ori Cohen. Its primary goal is to compile a wide array of machine learning educational resources in a single location, successfully encompassing over 500 topics.

This compendium features a wealth of articles, links, research papers, and courses. Given the extensive amount of material, it's advisable to pace yourself while exploring it. The content is well-structured, making it suitable for both novices and seasoned professionals alike. For instance, the Data Science section provides succinct explanations of essential concepts.

You'll find nearly all the information needed for your machine learning studies, including resources on Data Science tools, statistics, calculus, natural language processing (NLP), and evaluation metrics. While some material may redirect you to external courses, it remains beneficial for your learning experience. In summary, mlcompendium is an excellent resource for anyone venturing into machine learning.

If you’re interested in contributing, feel free to reach out to the author.

Section 1.2: Ruiterpan

Ruiterpan, established by Rui Pan, curates a collection of machine learning papers and blogs. This GitBook project also includes a guide on how to effectively read academic papers, which can be invaluable for those new to the field.

Additionally, Ruiterpan offers insights into navigating life as a Computer Science Ph.D. student, covering everything from application processes to what to expect post-Ph.D. This project is ideal for those eager to learn machine learning through an academic lens and apply experimental data science concepts.

Section 1.3: Scrapbook

In contrast to Ruiterpan's academic focus, Scrapbook, developed by Stephan Osteburg, emphasizes hands-on learning. This project prepares aspiring machine learning engineers through practical exercises, including coding challenges, SQL tasks, and more.

Moreover, Scrapbook covers job preparation aspects such as interviews, role descriptions, and agile methodologies. With numerous external links to books and courses, it's an excellent resource for those who prefer practical learning experiences over theoretical studies.

Section 1.4: Irosyadi

The Irosyadi GitBook project, named after author Imron Rosyadi, takes a unique approach by focusing on various applications of data. Rather than being a straightforward machine learning resource, it showcases a plethora of applications available for learning.

The project is well-organized, although you may encounter many external links, as the content often directs you to applications rather than providing exhaustive explanations.

Section 1.5: Machine Learning with TensorFlow.js

As the name suggests, this GitBook is dedicated to instructing readers on machine learning using TensorFlow.js. The project is segmented into four main sections:

• JavaScript for Machine Learning
• Developing Neural Network Solutions
• Deep Learning using TensorFlow.js
• Summary and Closing

This resource is particularly valuable for those looking to delve deeper into machine learning with TensorFlow, although some sections remain under development.