{"id":13678688,"url":"https://github.com/innat/ML-Resource","last_synced_at":"2025-04-29T15:32:44.259Z","repository":{"id":48664330,"uuid":"203540735","full_name":"innat/ML-Resource","owner":"innat","description":"A concise resource repository for machine learning","archived":false,"fork":false,"pushed_at":"2024-02-19T18:41:44.000Z","size":342134,"stargazers_count":110,"open_issues_count":0,"forks_count":35,"subscribers_count":11,"default_branch":"gh-pages","last_synced_at":"2024-08-02T13:24:25.918Z","etag":null,"topics":["data-analysis","data-science","deep-learning","kaggle","machine-learning","python","spark"],"latest_commit_sha":null,"homepage":"","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/innat.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-08-21T08:28:36.000Z","updated_at":"2023-12-13T17:17:18.000Z","dependencies_parsed_at":"2024-08-02T13:17:06.021Z","dependency_job_id":null,"html_url":"https://github.com/innat/ML-Resource","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/innat%2FML-Resource","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/innat%2FML-Resource/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/innat%2FML-Resource/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/innat%2FML-Resource/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/innat","download_url":"https://codeload.github.com/innat/ML-Resource/tar.gz/refs/heads/gh-pages","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224179037,"owners_count":17268992,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-science","deep-learning","kaggle","machine-learning","python","spark"],"created_at":"2024-08-02T13:00:57.189Z","updated_at":"2024-11-11T21:31:10.091Z","avatar_url":"https://github.com/innat.png","language":"HTML","funding_links":[],"categories":["HTML"],"sub_categories":[],"readme":"![dancing_drogon](https://user-images.githubusercontent.com/17668390/110049208-9ccc0d80-7d7b-11eb-855f-10116ea07661.gif)\n\n[![Palestine](https://img.shields.io/badge/Free-Palestine-white?labelColor=green)](https://twitter.com/search?q=%23FreePalestine\u0026src=typed_query)\n\n\nA concise resource repository for machine learning. Here, It'll remain concise but yet to comprehensive for machine learning resources and related stuff. It'll be updated continually with times. \n\n## Table of Contents\n- [Book Materials](#book-materials) - [Online Course](#online-course) - [Research Databases](#research-databases) - [GitHub](#github) - [Kaggle](#kaggle)- [Best Blog](#best-blog) - [Conferences](#conferences)- [YouTube Star](#youtube-star) \n\n\u003e Among the following content which are **BOLD**, you may like to click them first.\n\n\n## Book Materials \nSome of the most influential book lists in the related field. \n\n**Machine Learning (ML)**\n   + **[Hands-On ML with Scikit-Learn, Keras, and TensorFlow | Author: Aurélien](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)**  \n   + [Python Machine Learning | Author: Sebastian](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka-ebook/dp/B00YSILNL0)\n   + **[The Hundred-Page Machine Learning Book | Andriy](http://themlbook.com/wiki/doku.php?id=start)**\n   \n**Deep Learning** \n  + **[Deep Learning | Author: Ian Goodfellow](https://www.deeplearningbook.org/)**\n  + **[Deep Learning with Python | Author: Francois](https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438)** \n  + [Deep Learning for Computer Vision with Python | Author: Adrain](https://www.pyimagesearch.com/deep-learning-computer-vision-python-book/)\n  + [Reinforcement Learning: An Introduction | Richard](https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation-ebook/dp/B008H5Q8VA) \n  \n**Data Science**\n  + [Data Mining: Concepts and Techniques](https://www.amazon.com/Data-Mining-Concepts-Techniques-Management/dp/0123814790)\n  + **[An Introduction to Statistical Learning: with Applications in R](https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370)**\n  + [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython-ebook/dp/B075X4LT6K)\n  + [Learning Spark: Lightning-Fast Big Data Analysis](https://www.amazon.com/Learning-Spark-Lightning-Fast-Data-Analysis/dp/1449358624) \n  + **[High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark](https://www.amazon.com/High-Performance-Spark-Practices-Optimizing/dp/1491943203)**\n\n## Online Course \nSome of the most influential high rated, resourceful and promising MOOC courses.  \n\n**Machine Learning**\n   + [ML Crash Course | Google](https://developers.google.com/machine-learning/crash-course)\n   + [ML Specialization | Coursera](https://www.coursera.org/specializations/machine-learning-introduction) \n   + [ML with Javascript | Udemy](https://www.udemy.com/course/machine-learning-with-javascript/)\n   + [Google Machine Learning Education](https://developers.google.com/machine-learning)\n   \n**Deep Learning**\n   + [UFLDL Tutorial](http://ufldl.stanford.edu/tutorial/) | [Stanford Teaching](https://nlp.stanford.edu/teaching/) | [Stanford CS | CNN for Visual Recognition](https://cs231n.github.io/) | [Stanford CS | NLP with Deep Learning](http://web.stanford.edu/class/cs224n/index.html)\n   + **[Deep Learning Specialization | Coursera](https://www.coursera.org/specializations/deep-learning)**\n   + [Complete Guide to TensorFlow for Deep Learning with Python | Udemy](https://www.udemy.com/course/complete-guide-to-tensorflow-for-deep-learning-with-python/)\n   + [Python for Computer Vision with OpenCV and Deep Learning | Udemy](https://www.udemy.com/course/python-for-computer-vision-with-opencv-and-deep-learning/)\n   + **[Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD \u0026 GANs | Udemy](https://www.udemy.com/course/master-deep-learning-computer-visiontm-cnn-ssd-yolo-gans/)**\n\n**Data Science**\n   + [Python for Data Science | edX](https://www.edx.org/course/python-for-data-science-3)\n   + [Big Data Specialization | Coursera](https://www.coursera.org/specializations/big-data)\n   + [Python for Data Science and Machine Learning Bootcamp | Udemy](https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/) \n   + [Spark and Python for Big Data with PySpark | Udemy](https://www.udemy.com/course/spark-and-python-for-big-data-with-pyspark/)\n\n## Research Databases\nSome enrich online research databases.\n- [Arxiv Sanity Preserver](http://www.arxiv-sanity.com/)\n- [FB Research](https://research.fb.com/publications/) | [Apple Machine Learning Journal](https://machinelearning.apple.com/) | [Google AI](https://ai.google/research/pubs/) | [Open AI](https://openai.com/)\n- **[Distill](https://distill.pub/)**\n- **[PaperWithCode](https://paperswithcode.com/)**\n- [ICLR:OpenReview](https://openreview.net/group?id=ICLR.cc)\n- Dataset : [OpenDataLab](https://opendatalab.com/)\n- Dataset : [PaperWithCode DB](https://paperswithcode.com/datasets)\n\n## GitHub (WIP)\n\nSome open source project to facilitate computer vision task, i.e. classification, detection, semanctic / instance / panoptic segmentation, object tracking, keypoint detection, optical character recognition, etc. \n\n| OS Projects | Research Paper | Explained Blogs |\n|-------|-------|-------|\n|[Detectron2](https://github.com/facebookresearch/detectron2) | [Blog](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/) | [Doc](https://detectron2.readthedocs.io/en/latest/) |\n|[OpenMMLab](https://github.com/open-mmlab) | - | - |\n|[2D Segmentation-PyTorch](https://github.com/qubvel/segmentation_models.pytorch) | - | [Doc](https://smp.readthedocs.io/en/latest/) |\n|[2D Segmentation-Keras](https://github.com/qubvel/segmentation_models) | - | [Doc](https://segmentation-models.readthedocs.io/en/latest/) |\n|**[CAM](https://github.com/metalbubble/CAM)** | [Arxiv](https://arxiv.org/pdf/1512.04150.pdf) | [Doc](http://cnnlocalization.csail.mit.edu/) |\n|**[PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics)** | - | - |\n| - | - | - | \n|\u003cul\u003e\u003cli\u003e[ETH VIZ](https://www.vis.xyz/)\u003c/li\u003e\u003cli\u003e[ML-From-Scratch](https://github.com/eriklindernoren/ML-From-Scratch)\u003c/li\u003e\u003cli\u003e[Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning)\u003c/li\u003e\u003cli\u003e[Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)\u003c/li\u003e\u003c/ul\u003e | \u003cul\u003e\u003cli\u003e[Awesome Python](https://github.com/vinta/awesome-python#readme)\u003c/li\u003e\u003cli\u003e[Awesome C++](https://github.com/fffaraz/awesome-cpp#readme)\u003c/li\u003e\u003cli\u003e[Awesome JavaScript](https://github.com/sorrycc/awesome-javascript#readme)\u003c/li\u003e\u003cli\u003e[Awesome Scala](https://github.com/lauris/awesome-scala#readme)\u003c/li\u003e\u003c/ul\u003e | \u003cul\u003e\u003cli\u003e[Algorithm Visualizer](https://github.com/algorithm-visualizer/algorithm-visualizer)\u003c/li\u003e\u003cli\u003e[Algovis](https://github.com/enjalot/algovis)\u003c/li\u003e\u003cli\u003e[Awesome TF-Lite](https://github.com/margaretmz/awesome-tensorflow-lite)\u003c/li\u003e\u003c/ul\u003e |\n\n\n## Kaggle\n\n- Deep Learning in Medicine  \n  - [NIH Chest X-rays](https://www.kaggle.com/nih-chest-xrays/data)\n  - [SIIM-ISIC Melanoma Classification](https://www.kaggle.com/c/siim-isic-melanoma-classification)\n  - [2018 Data Science Bowl](https://www.kaggle.com/c/data-science-bowl-2018) - **Segmentation**\n  - [Chest X-Ray Images (Pneumonia)](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia)\n  - [Recursion Cellular Image Classification](https://www.kaggle.com/c/recursion-cellular-image-classification)\n  - [APTOS 2019 Blindness Detection](https://www.kaggle.com/c/aptos2019-blindness-detection)\n  - [Diabetic Retinopathy Detection](https://www.kaggle.com/c/diabetic-retinopathy-detection)\n  - [TReNDS Neuroimaging](https://www.kaggle.com/c/trends-assessment-prediction)\n  - [SIIM-ACR Pneumothorax Segmentation](https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/overview)\n  - [RSNA Pneumonia Detection Challenge](https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/overview)\n  - [OSIC Pulmonary Fibrosis Progression](https://www.kaggle.com/c/osic-pulmonary-fibrosis-progression)\n  - [VinBigData Chest X-ray Abnormalities Detection](https://www.kaggle.com/c/vinbigdata-chest-xray-abnormalities-detection/overview)\n  - [RANZCR CLiP - Catheter and Line Position Challenge](https://www.kaggle.com/c/ranzcr-clip-catheter-line-classification/overview)\n  - [OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction](https://www.kaggle.com/c/stanford-covid-vaccine)\n  - [RSNA STR Pulmonary Embolism Detection](https://www.kaggle.com/c/rsna-str-pulmonary-embolism-detection)\n  - [Mechanisms of Action (MoA) Prediction](https://www.kaggle.com/c/lish-moa)\n  - [Predicting Molecular Properties](https://www.kaggle.com/c/champs-scalar-coupling)\n  - [Prostate cANcer graDe Assessment (PANDA) Challenge](https://www.kaggle.com/c/prostate-cancer-grade-assessment)\n  - [RSNA Intracranial Hemorrhage Detection](https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection)\n  - [HuBMAP - Hacking the Kidney](https://www.kaggle.com/c/hubmap-kidney-segmentation)\n  - [SIIM-FISABIO-RSNA COVID-19 Detection](https://www.kaggle.com/c/siim-covid19-detection)\n  - [RSNA-MICCAI Brain Tumor Radiogenomic Classification](https://www.kaggle.com/c/rsna-miccai-brain-tumor-radiogenomic-classification/overview)\n  - [HuBMAP + HPA - Hacking the Human Body](https://www.kaggle.com/competitions/hubmap-organ-segmentation)\n  - [UW-Madison GI Tract Image Segmentation](https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation) - **Segmentation**\n  - [RSNA 2022 Cervical Spine Fracture Detection](https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/)\n  \n- General Competitions \n\n  **Classification (Image)**\n  - [Bengali.AI Handwritten Grapheme Classification](https://www.kaggle.com/c/bengaliai-cv19)\n  - [Understanding Clouds from Satellite Images](https://www.kaggle.com/c/understanding_cloud_organization)\n  - [ALASKA2 Image Steganalysis](https://www.kaggle.com/c/alaska2-image-steganalysis)\n  - [Cassava Leaf Disease Classification](https://www.kaggle.com/c/cassava-leaf-disease-classification)\n  - [H\u0026M Personalized Fashion Recommendations](https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/)\n  \n  **Image Similarity**\n  - [Google Universal Image Embedding](https://www.kaggle.com/competitions/google-universal-image-embedding)\n  - [Image Matching Challenge](https://www.kaggle.com/competitions/image-matching-challenge-2022)\n  - Google Landmark Retrieval [2018](https://www.kaggle.com/competitions/landmark-retrieval-challenge) - [2019](https://www.kaggle.com/competitions/landmark-retrieval-2019) - [2020](https://www.kaggle.com/competitions/landmark-retrieval-2020) - [2021](https://www.kaggle.com/competitions/landmark-retrieval-2021)\n  - Google Landmark Recognition [2019](https://www.kaggle.com/competitions/landmark-recognition-2019) - [2020](https://www.kaggle.com/competitions/landmark-recognition-2020) - [2021](https://www.kaggle.com/competitions/landmark-recognition-2021)\n  - [FGVC6](https://sites.google.com/view/fgvc6/home?authuser=0)\n  \n  **Detection (CV)**\n  - [Google AI Open Images - Object Detection Track](https://www.kaggle.com/competitions/google-ai-open-images-object-detection-track)\n  - [Tensorflow - Help Protect the Great Barrier Reef](https://www.kaggle.com/c/tensorflow-great-barrier-reef/overview)\n  - [Global Wheat Detection](https://www.kaggle.com/c/global-wheat-detection)\n  - [Severstal: Steel Defect Detection](https://www.kaggle.com/c/severstal-steel-defect-detection)\n  - [Deepfake Detection Challenge](https://www.kaggle.com/c/deepfake-detection-challenge)\n  - [Humpback Whale Identification](https://www.kaggle.com/c/humpback-whale-identification/overview)\n  - [NFL 1st and Future - Impact Detection](https://www.kaggle.com/c/nfl-impact-detection)\n  - [Lyft Motion Prediction for Autonomous Vehicles](https://www.kaggle.com/c/lyft-motion-prediction-autonomous-vehicles)\n  - [Lyft 3D Object Detection for Autonomous Vehicles](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles)\n  - [Peking University/Baidu - Autonomous Driving](https://www.kaggle.com/c/pku-autonomous-driving)\n  \n  **Segmentation (CV)**\n  - [Airbus Ship Detection Challenge](https://www.kaggle.com/c/airbus-ship-detection/overview)\n  - [Carvana Image Masking Challenge](https://www.kaggle.com/c/carvana-image-masking-challenge)\n  - [Dstl Satellite Imagery Feature Detection](https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection)\n  - [Understanding Clouds from Satellite Images](https://www.kaggle.com/c/understanding_cloud_organization/overview)\n  - [iMaterialist (Fashion) 2019 at FGVC6](https://www.kaggle.com/c/imaterialist-fashion-2019-FGVC6/overview)\n  - [Carvana Image Masking Challenge](https://www.kaggle.com/c/carvana-image-masking-challenge)\n\n  **Instance Segmentation**\n  - [Sartorius - Cell Instance Segmentation](https://www.kaggle.com/c/sartorius-cell-instance-segmentation)\n  \n  **Natural Language Processing**\n  - [Google AI4Code](https://www.kaggle.com/competitions/AI4Code)\n  - [Feedback Prize - Evaluating Student Writing](https://www.kaggle.com/c/feedback-prize-2021)\n  - [Feedback Prize - Predicting Effective Arguments](https://www.kaggle.com/competitions/feedback-prize-effectiveness)\n  - [TensorFlow 2.0 Question Answering](https://www.kaggle.com/c/tensorflow2-question-answering)\n  - [Google QUEST Q\u0026A Labeling](https://www.kaggle.com/c/google-quest-challenge)\n  - [Jigsaw Multilingual Toxic Comment Classification](https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification)\n  - [Jigsaw Unintended Bias in Toxicity Classification](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification)\n  - [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)\n  - [Tweet Sentiment Extraction](https://www.kaggle.com/c/tweet-sentiment-extraction)\n  - [U.S. Patent Phrase to Phrase Matching](https://www.kaggle.com/competitions/us-patent-phrase-to-phrase-matching/overview)\n  - [Zillow Prize](https://www.kaggle.com/competitions/zillow-prize-1/code?competitionId=6649\u0026sortBy=voteCount)\n  \n  **Audio**\n    - [Cornell Birdcall Identification](https://www.kaggle.com/c/birdsong-recognition)\n    - [BirdCLEF 2022: Identify bird calls in soundscapes](https://www.kaggle.com/c/birdclef-2022/overview)\n  \n  **Generative  AI**\n  - [Generative Dog Images](https://www.kaggle.com/c/generative-dog-images)\n  - [Stable Diffusion - Image to Prompts](https://www.kaggle.com/competitions/stable-diffusion-image-to-prompts)\n\n  **Time Series**\n  - [OTTO – Multi-Objective Recommender System](https://www.kaggle.com/competitions/otto-recommender-system/)\n\n  **Special**\n  - [Abstraction and Reasoning Challenge](https://www.kaggle.com/c/abstraction-and-reasoning-challenge)\n\n\n## Best Blog \nVery few amount of amazing blogs.\n\n- [OpenAI](https://openai.com/research)\n- [Fast-AI](https://www.fast.ai/)\n- [Google AI Blog](https://ai.googleblog.com/)\n- [TensorFlow Blog](https://blog.tensorflow.org/)\n- [Facebook AI](https://ai.facebook.com/)\n- [Berkeley AI Research](https://bair.berkeley.edu/blog/)\n- **[Depth First Learning](http://www.depthfirstlearning.com/)**\n- [Polo Club of Data Science](https://poloclub.github.io/)\n- [OpenAI](https://openai.com/)\n- [Research Blog: Stanford NLP](https://nlp.stanford.edu/blog/)\n- **[PaperWithCode](https://paperswithcode.com/)**\n- **[Cleverhans](http://www.cleverhans.io/)** | About: Security and privacy in machine learning.\n- **[Andrej Karpathy](https://karpathy.github.io/)**\n- [Visualizing ML: Jay Alammar](http://jalammar.github.io/)\n- **[Lil'Log](https://lilianweng.github.io/lil-log/)**\n- **[Kevin Zakka](https://kevinzakka.github.io/)**\n- [Arthur Juliani](https://medium.com/@awjuliani)\n- [Colah](https://colah.github.io/)\n- [Michael Nielsen](http://michaelnielsen.org/)\n- [Sebastian Ruder](http://ruder.io/#open)\n- [PyImageSearch](https://www.pyimagesearch.com/)\n- [Machine Learning Mastery](https://machinelearningmastery.com/blog/)\n- [Open Source](#best-blog)\n   + [Facebook Opens Source](https://opensource.facebook.com/)\n   + [Google Open Source](https://opensource.google/)\n- [Annotated PyTorch Paper Implementations](https://nn.labml.ai/)\n\n## Conferences\n- [CVPR - IEEE Conference on Computer Vision and Pattern Recognition](http://cvpr2018.thecvf.com/)\n- [NeurIPS : Neural Information Processing Systems (NIPS)](https://nips.cc/)\n- [ECCV : European Conference on Computer Vision](https://eccv2020.eu/)\n- [ICML : International Conference on Machine Learning](https://icml.cc/)\n- [ICCV : IEEE/CVF International Conference on Computer Vision](http://iccv2019.thecvf.com/)\n\n\n## YouTube Star\n- [DSA | mycodeschool](https://www.youtube.com/user/mycodeschool)\n- [Arxiv Insights](https://www.youtube.com/c/ArxivInsights)\n- [Welch Labs](https://www.youtube.com/c/WelchLabsVideo/featured)\n- [3Blue1Brown](https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw) \n- [Edureka](https://www.youtube.com/channel/UCkw4JCwteGrDHIsyIIKo4tQ) \n- [Sentdex](https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ) \n- [Standford School | CNN for Visual Recognition](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) \n- [Code Bullet](https://www.youtube.com/channel/UC0e3QhIYukixgh5VVpKHH9Q/featured)\n- [Khan Academy](https://www.youtube.com/channel/UC4a-Gbdw7vOaccHmFo40b9g)\n\n## Contact\nIf you've anything in mind that you think is awesome and would fit here, feel free to send a [pull request](https://github.com/innat/ML-Bookmarks/pulls) or if you're just feeling social, feel free to [email](innat1994@gmail.com) me or reach out on [LinkedIn](https://www.linkedin.com/in/innat2k14/). \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finnat%2FML-Resource","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Finnat%2FML-Resource","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finnat%2FML-Resource/lists"}