The ML YouTube Courses repository has seen a significant decline in recent contributions, with the last major activity occurring over six months ago, raising concerns about its ongoing relevance and community engagement. This repository, maintained by DAIR.AI, serves as a curated index of high-quality machine learning and AI courses available on YouTube, aiming to promote accessible education in these rapidly evolving fields.
Recent activities indicate a troubling stagnation; while the repository has historically been a hub for community-driven contributions, the last substantial updates were made over 200 days ago. The open issues reflect vague requests that may hinder effective resolution, and there are currently no open pull requests, suggesting a lull in active development or maintenance.
The repository currently has 9 open issues, with the most recent (#41) created just 48 days ago. However, many of these issues are vague or lack sufficient detail for effective resolution, such as #39 ("Mm") and #40 ("ML course"). The last substantial issue creation occurred between 48 to 106 days ago, indicating a potential decline in active contributions or requests for new resources.
The majority of team members have not engaged in any commits for several months, highlighting a concerning trend of inactivity.
| Timespan | Opened | Closed | Comments | Labeled | Milestones |
|---|---|---|---|---|---|
| 7 Days | 0 | 0 | 0 | 0 | 0 |
| 30 Days | 0 | 0 | 0 | 0 | 0 |
| 90 Days | 1 | 0 | 0 | 1 | 1 |
| 1 Year | 7 | 0 | 0 | 7 | 1 |
| All Time | 17 | 8 | - | - | - |
Like all software activity quantification, these numbers are imperfect but sometimes useful. Comments, Labels, and Milestones refer to those issues opened in the timespan in question.
The GitHub repository for ML YouTube Courses currently has 9 open issues, with the most recent activity occurring in the last 48 days. A notable trend is the prevalence of issues that are vague or lack detailed descriptions, such as #39 ("Mm") and #40 ("ML course"), which could hinder effective resolution and community engagement. Additionally, there is a significant gap in the frequency of issue creation, with the most recent issues being created between 48 to 106 days ago, suggesting a potential decline in active contributions or requests for new resources.
Issue #41: ML-YouTube-Courses
Issue #40: ML course
Issue #39: Mm
Issue #38: Machine learning
Issue #37: ML
Issue #36: 머신러닝
Issue #34: ML courses
Issue #31: Add New CS330 course
Issue #29: Machine learning videos
Issue #27: AI and ML
Issue #20: Deep learning
Issue #18: Tutorial
Issue #15: Add other platforms that one can learn.
Issue #6: add Deep Learning for Coders by Fast.AI
Issue #5: Add separate note sheets for each of the sections/lectures
Issue #4: Add prerequisites and level
Issue #2: Course: Introduction to Machine Learning with scikit-learn
The open issues reflect a mix of requests for new content and vague entries that may require clarification or further elaboration to facilitate community contributions or project maintenance. The closed issues indicate a history of active engagement but also highlight a potential need for clearer guidelines on issue submissions to enhance the quality of contributions moving forward.
The analysis of the pull requests (PRs) for the dair-ai/ML-YouTube-Courses repository reveals a total of 24 closed PRs, primarily focused on adding new educational resources and updating documentation. There are currently no open PRs, indicating a potential lull in contributions or completion of recent updates.
PR #35: Update README.md
Closed 207 days ago. This PR involved updating the README file to reflect the latest changes or additions to the repository.
PR #33: Add "Deep Learning for Computer Vision (neuralearn.ai)"
Closed 290 days ago. This PR contributed a new course link, enhancing the repository's offerings in deep learning.
PR #32: Probabilistic Machine Learning course link was missing
Closed 374 days ago. This PR addressed a gap by adding a previously missing course link.
PR #30: Add Caltech CS156
Closed 482 days ago. This PR added another prestigious course to the list, further enriching the repository's content.
PR #28: Add Introduction to Data-Centric AI
Closed 541 days ago. This addition reflects the growing importance of data-centric approaches in AI education.
PR #26: Add MIT 6.S897: Machine Learning for Healthcare (2019)
Closed 556 days ago. This PR introduced a specialized course focusing on healthcare applications of machine learning.
PR #25: Add MIT course 18.337J/6.338J
Closed 563 days ago. Another significant addition from MIT, showcasing the repository’s focus on high-quality educational content.
PR #24: Update README.md
Closed 563 days ago. Similar to PR #35, this update likely included additional context or resources in the README.
PR #23: Add CMUs Intro to DL Course
Closed 628 days ago. This PR expanded the repository's offerings with a course from Carnegie Mellon University.
PR #22: Fix a few typos
Closed 650 days ago. A minor but necessary update to improve documentation quality.
PR #21: Typos
Closed 654 days ago. Another PR focused on correcting typographical errors in the documentation.
PR #19: Add CS231n: Convolutional Neural Networks for Visual Recognition
Closed 715 days ago. This significant addition is one of the most recognized courses in deep learning.
PR #17: Update README.md
Closed 755 days ago. Another documentation update aimed at keeping information current.
PR #16: Add new courses
Closed 763 days ago. This PR likely added multiple courses at once, showcasing community engagement.
PR #14: Added two new RL courses
Closed 852 days ago. Focused on reinforcement learning, this addition reflects current trends in AI education.
PR #13: Added Stanford CS230: Deep Learning
Closed 857 days ago. A critical addition from Stanford that enhances the depth of content available in deep learning.
PR #12: Add new course
Closed 869 days ago. Details unspecified, but indicates ongoing contributions to expand course offerings.
PR #11: Fixed german spelling
Closed 870 days ago. A minor fix aimed at improving language accuracy in documentation.
PR #10: add CS 329S: Machine Learning Systems Design
Closed 853 days ago. This course addition focuses on practical systems design in machine learning applications.
PR #9: Added Applied Language Technology to the list
Closed 873 days ago. This addition reflects an interest in natural language processing and its applications.
PR #8: Added 5 new courses
Closed 873 days ago. A bulk addition that significantly increased the number of available resources.
PR #7: Create LICENSE
Closed 882 days ago. Establishing licensing is crucial for open-source projects and community contributions.
PR #3: Columbia University : Natural Language Processing ADDED
Closed 976 days ago. A valuable contribution focusing on NLP from a reputable institution.
PR #1: Add intro to DL and generative modeling course
Closed 1097 days ago. One of the initial contributions that set the stage for future additions to the repository.
The pull requests for the dair-ai/ML-YouTube-Courses repository illustrate a robust effort towards curating high-quality educational resources in machine learning and artificial intelligence from reputable institutions such as Stanford, MIT, and Caltech. The majority of these PRs focus on adding new courses, which aligns with the project's mission to provide an extensive index of accessible educational materials on YouTube.
A notable trend is the frequent updates to the README file (evidenced by multiple entries like PRs #35, #24, and others), highlighting an ongoing commitment to keeping information current and relevant for users navigating through numerous resources available in this repository. Such updates are essential for maintaining clarity and usability as more courses are added over time.
Additionally, there is a clear emphasis on addressing gaps within the existing resource list, as seen in PRs like #32, which rectified missing links, and various entries that added specific courses that cater to emerging areas within AI education such as data-centric AI (PR #28) and machine learning applications in healthcare (PR #26). This responsiveness not only enhances user experience but also reflects an understanding of evolving educational needs within the field of AI and machine learning.
The presence of several typo-fixing PRs (e.g., PRs #22 and #21) indicates an active community that values documentation quality alongside content richness—an important aspect for any open-source project aiming for broad community engagement and usability.
However, it is worth noting that there have been no open pull requests at this time, which could suggest either a temporary pause in contributions or that recent efforts have successfully addressed existing needs within the project scope without leaving outstanding issues or enhancements pending review or implementation.
Overall, this repository stands out as a well-maintained resource that not only serves its educational purpose effectively but also fosters community involvement through contributions, thereby enriching its content continuously while adapting to new trends and requirements in AI education.
Elvis Saravia (omarsar)
Iman Mohammadi (Imanm02)
Soham Talukdar (sohamtalukdar)
Pietro Monticone (pitmonticone)
Vanshika Gupta (vg11072001)
Bharat Raghunathan (bharatr21)
Anish Athalye (anishathalye)
Parham Saremi (parhamsaremi)
Michael Todd (mtoddx)
Jean de Dieu Nyandwi (Nyandwi)
The most recent activities of the team members indicate a significant focus on updating the README.md file with new courses and fixing issues related to existing entries. The last substantial contributions occurred over six months ago, suggesting that while the repository has seen active maintenance in the past, recent engagement has diminished. The majority of contributions are centered around merging pull requests that add educational resources or correct errors in documentation.
The development team has historically been active in curating educational resources but appears to have slowed down recently. The focus remains on documentation updates and community contributions, which are essential for maintaining the repository's relevance in the educational landscape of machine learning.
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