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https://github.com/innat/ML-Resource

A concise resource repository for machine learning
https://github.com/innat/ML-Resource
data-analysis data-science deep-learning kaggle machine-learning python spark
Last synced: 3 months ago
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A concise resource repository for machine learning
Host: GitHub
URL: https://github.com/innat/ML-Resource
Owner: innat
License: mit
Created: 2019-08-21T08:28:36.000Z (about 5 years ago)
Default Branch: gh-pages
Last Pushed: 2024-02-19T18:41:44.000Z (9 months ago)
Last Synced: 2024-06-17T15:53:29.424Z (5 months ago)
Topics: data-analysis, data-science, deep-learning, kaggle, machine-learning, python, spark
Language: HTML
Homepage:
Size: 326 MB
Stars: 110
Watchers: 11
Forks: 35
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

README

        ![dancing_drogon](https://user-images.githubusercontent.com/17668390/110049208-9ccc0d80-7d7b-11eb-855f-10116ea07661.gif)

[![Palestine](https://img.shields.io/badge/Free-Palestine-white?labelColor=green)](https://twitter.com/search?q=%23FreePalestine&src=typed_query)

A 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. 

## Table of Contents

- [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) 

> Among the following content which are **BOLD**, you may like to click them first.

## Book Materials 

Some of the most influential book lists in the related field. 

**Machine Learning (ML)**

   + **[Hands-On ML with Scikit-Learn, Keras, and TensorFlow | Author: Aurélien](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)**  

   + [Python Machine Learning | Author: Sebastian](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka-ebook/dp/B00YSILNL0)

   + **[The Hundred-Page Machine Learning Book | Andriy](http://themlbook.com/wiki/doku.php?id=start)**

   

**Deep Learning** 

  + **[Deep Learning | Author: Ian Goodfellow](https://www.deeplearningbook.org/)**

  + **[Deep Learning with Python | Author: Francois](https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438)** 

  + [Deep Learning for Computer Vision with Python | Author: Adrain](https://www.pyimagesearch.com/deep-learning-computer-vision-python-book/)

  + [Reinforcement Learning: An Introduction | Richard](https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation-ebook/dp/B008H5Q8VA) 

  

**Data Science**

  + [Data Mining: Concepts and Techniques](https://www.amazon.com/Data-Mining-Concepts-Techniques-Management/dp/0123814790)

  + **[An Introduction to Statistical Learning: with Applications in R](https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370)**

  + [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython-ebook/dp/B075X4LT6K)

  + [Learning Spark: Lightning-Fast Big Data Analysis](https://www.amazon.com/Learning-Spark-Lightning-Fast-Data-Analysis/dp/1449358624) 

  + **[High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark](https://www.amazon.com/High-Performance-Spark-Practices-Optimizing/dp/1491943203)**

## Online Course 

Some of the most influential high rated, resourceful and promising MOOC courses.  

**Machine Learning**

   + [ML Crash Course | Google](https://developers.google.com/machine-learning/crash-course)

   + [ML Specialization | Coursera](https://www.coursera.org/specializations/machine-learning-introduction) 

   + [ML with Javascript | Udemy](https://www.udemy.com/course/machine-learning-with-javascript/)

   + [Google Machine Learning Education](https://developers.google.com/machine-learning)

   

**Deep Learning**

   + [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)

   + **[Deep Learning Specialization | Coursera](https://www.coursera.org/specializations/deep-learning)**

   + [Complete Guide to TensorFlow for Deep Learning with Python | Udemy](https://www.udemy.com/course/complete-guide-to-tensorflow-for-deep-learning-with-python/)

   + [Python for Computer Vision with OpenCV and Deep Learning | Udemy](https://www.udemy.com/course/python-for-computer-vision-with-opencv-and-deep-learning/)

   + **[Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs | Udemy](https://www.udemy.com/course/master-deep-learning-computer-visiontm-cnn-ssd-yolo-gans/)**

**Data Science**

   + [Python for Data Science | edX](https://www.edx.org/course/python-for-data-science-3)

   + [Big Data Specialization | Coursera](https://www.coursera.org/specializations/big-data)

   + [Python for Data Science and Machine Learning Bootcamp | Udemy](https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/) 

   + [Spark and Python for Big Data with PySpark | Udemy](https://www.udemy.com/course/spark-and-python-for-big-data-with-pyspark/)

## Research Databases

Some enrich online research databases.

- [Arxiv Sanity Preserver](http://www.arxiv-sanity.com/)

- [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/)

- **[Distill](https://distill.pub/)**

- **[PaperWithCode](https://paperswithcode.com/)**

- [ICLR:OpenReview](https://openreview.net/group?id=ICLR.cc)

- Dataset : [OpenDataLab](https://opendatalab.com/)

- Dataset : [PaperWithCode DB](https://paperswithcode.com/datasets)

## GitHub (WIP)

Some open source project to facilitate computer vision task, i.e. classification, detection, semanctic / instance / panoptic segmentation, object tracking, keypoint detection, optical character recognition, etc. 

| OS Projects | Research Paper | Explained Blogs |

|-------|-------|-------|

|[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/) |

|[OpenMMLab](https://github.com/open-mmlab) | - | - |

|[2D Segmentation-PyTorch](https://github.com/qubvel/segmentation_models.pytorch) | - | [Doc](https://smp.readthedocs.io/en/latest/) |

|[2D Segmentation-Keras](https://github.com/qubvel/segmentation_models) | - | [Doc](https://segmentation-models.readthedocs.io/en/latest/) |

|**[CAM](https://github.com/metalbubble/CAM)** | [Arxiv](https://arxiv.org/pdf/1512.04150.pdf) | [Doc](http://cnnlocalization.csail.mit.edu/) |

|**[PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics)** | - | - |

| - | - | - | 

|


[ETH VIZ](https://www.vis.xyz/)

[ML-From-Scratch](https://github.com/eriklindernoren/ML-From-Scratch)

[Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning)

[Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)

 | 

[Awesome Python](https://github.com/vinta/awesome-python#readme)

[Awesome C++](https://github.com/fffaraz/awesome-cpp#readme)

[Awesome JavaScript](https://github.com/sorrycc/awesome-javascript#readme)

[Awesome Scala](https://github.com/lauris/awesome-scala#readme)

 | 

[Algorithm Visualizer](https://github.com/algorithm-visualizer/algorithm-visualizer)

[Algovis](https://github.com/enjalot/algovis)

[Awesome TF-Lite](https://github.com/margaretmz/awesome-tensorflow-lite)

 |

## Kaggle

- Deep Learning in Medicine  

  - [NIH Chest X-rays](https://www.kaggle.com/nih-chest-xrays/data)

  - [SIIM-ISIC Melanoma Classification](https://www.kaggle.com/c/siim-isic-melanoma-classification)

  - [2018 Data Science Bowl](https://www.kaggle.com/c/data-science-bowl-2018) - **Segmentation**

  - [Chest X-Ray Images (Pneumonia)](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia)

  - [Recursion Cellular Image Classification](https://www.kaggle.com/c/recursion-cellular-image-classification)

  - [APTOS 2019 Blindness Detection](https://www.kaggle.com/c/aptos2019-blindness-detection)

  - [Diabetic Retinopathy Detection](https://www.kaggle.com/c/diabetic-retinopathy-detection)

  - [TReNDS Neuroimaging](https://www.kaggle.com/c/trends-assessment-prediction)

  - [SIIM-ACR Pneumothorax Segmentation](https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/overview)

  - [RSNA Pneumonia Detection Challenge](https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/overview)

  - [OSIC Pulmonary Fibrosis Progression](https://www.kaggle.com/c/osic-pulmonary-fibrosis-progression)

  - [VinBigData Chest X-ray Abnormalities Detection](https://www.kaggle.com/c/vinbigdata-chest-xray-abnormalities-detection/overview)

  - [RANZCR CLiP - Catheter and Line Position Challenge](https://www.kaggle.com/c/ranzcr-clip-catheter-line-classification/overview)

  - [OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction](https://www.kaggle.com/c/stanford-covid-vaccine)

  - [RSNA STR Pulmonary Embolism Detection](https://www.kaggle.com/c/rsna-str-pulmonary-embolism-detection)

  - [Mechanisms of Action (MoA) Prediction](https://www.kaggle.com/c/lish-moa)

  - [Predicting Molecular Properties](https://www.kaggle.com/c/champs-scalar-coupling)

  - [Prostate cANcer graDe Assessment (PANDA) Challenge](https://www.kaggle.com/c/prostate-cancer-grade-assessment)

  - [RSNA Intracranial Hemorrhage Detection](https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection)

  - [HuBMAP - Hacking the Kidney](https://www.kaggle.com/c/hubmap-kidney-segmentation)

  - [SIIM-FISABIO-RSNA COVID-19 Detection](https://www.kaggle.com/c/siim-covid19-detection)

  - [RSNA-MICCAI Brain Tumor Radiogenomic Classification](https://www.kaggle.com/c/rsna-miccai-brain-tumor-radiogenomic-classification/overview)

  - [HuBMAP + HPA - Hacking the Human Body](https://www.kaggle.com/competitions/hubmap-organ-segmentation)

  - [UW-Madison GI Tract Image Segmentation](https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation) - **Segmentation**

  - [RSNA 2022 Cervical Spine Fracture Detection](https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/)

  

- General Competitions 

  **Classification (Image)**

  - [Bengali.AI Handwritten Grapheme Classification](https://www.kaggle.com/c/bengaliai-cv19)

  - [Understanding Clouds from Satellite Images](https://www.kaggle.com/c/understanding_cloud_organization)

  - [ALASKA2 Image Steganalysis](https://www.kaggle.com/c/alaska2-image-steganalysis)

  - [Cassava Leaf Disease Classification](https://www.kaggle.com/c/cassava-leaf-disease-classification)

  - [H&M Personalized Fashion Recommendations](https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/)

  

  **Image Similarity**

  - [Google Universal Image Embedding](https://www.kaggle.com/competitions/google-universal-image-embedding)

  - [Image Matching Challenge](https://www.kaggle.com/competitions/image-matching-challenge-2022)

  - 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)

  - 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)

  - [FGVC6](https://sites.google.com/view/fgvc6/home?authuser=0)

  

  **Detection (CV)**

  - [Google AI Open Images - Object Detection Track](https://www.kaggle.com/competitions/google-ai-open-images-object-detection-track)

  - [Tensorflow - Help Protect the Great Barrier Reef](https://www.kaggle.com/c/tensorflow-great-barrier-reef/overview)

  - [Global Wheat Detection](https://www.kaggle.com/c/global-wheat-detection)

  - [Severstal: Steel Defect Detection](https://www.kaggle.com/c/severstal-steel-defect-detection)

  - [Deepfake Detection Challenge](https://www.kaggle.com/c/deepfake-detection-challenge)

  - [Humpback Whale Identification](https://www.kaggle.com/c/humpback-whale-identification/overview)

  - [NFL 1st and Future - Impact Detection](https://www.kaggle.com/c/nfl-impact-detection)

  - [Lyft Motion Prediction for Autonomous Vehicles](https://www.kaggle.com/c/lyft-motion-prediction-autonomous-vehicles)

  - [Lyft 3D Object Detection for Autonomous Vehicles](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles)

  - [Peking University/Baidu - Autonomous Driving](https://www.kaggle.com/c/pku-autonomous-driving)

  

  **Segmentation (CV)**

  - [Airbus Ship Detection Challenge](https://www.kaggle.com/c/airbus-ship-detection/overview)

  - [Carvana Image Masking Challenge](https://www.kaggle.com/c/carvana-image-masking-challenge)

  - [Dstl Satellite Imagery Feature Detection](https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection)

  - [Understanding Clouds from Satellite Images](https://www.kaggle.com/c/understanding_cloud_organization/overview)

  - [iMaterialist (Fashion) 2019 at FGVC6](https://www.kaggle.com/c/imaterialist-fashion-2019-FGVC6/overview)

  - [Carvana Image Masking Challenge](https://www.kaggle.com/c/carvana-image-masking-challenge)

  **Instance Segmentation**

  - [Sartorius - Cell Instance Segmentation](https://www.kaggle.com/c/sartorius-cell-instance-segmentation)

  

  **Natural Language Processing**

  - [Google AI4Code](https://www.kaggle.com/competitions/AI4Code)

  - [Feedback Prize - Evaluating Student Writing](https://www.kaggle.com/c/feedback-prize-2021)

  - [Feedback Prize - Predicting Effective Arguments](https://www.kaggle.com/competitions/feedback-prize-effectiveness)

  - [TensorFlow 2.0 Question Answering](https://www.kaggle.com/c/tensorflow2-question-answering)

  - [Google QUEST Q&A Labeling](https://www.kaggle.com/c/google-quest-challenge)

  - [Jigsaw Multilingual Toxic Comment Classification](https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification)

  - [Jigsaw Unintended Bias in Toxicity Classification](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification)

  - [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)

  - [Tweet Sentiment Extraction](https://www.kaggle.com/c/tweet-sentiment-extraction)

  - [U.S. Patent Phrase to Phrase Matching](https://www.kaggle.com/competitions/us-patent-phrase-to-phrase-matching/overview)

  - [Zillow Prize](https://www.kaggle.com/competitions/zillow-prize-1/code?competitionId=6649&sortBy=voteCount)

  

  **Audio**

    - [Cornell Birdcall Identification](https://www.kaggle.com/c/birdsong-recognition)

    - [BirdCLEF 2022: Identify bird calls in soundscapes](https://www.kaggle.com/c/birdclef-2022/overview)

  

  **Generative  AI**

  - [Generative Dog Images](https://www.kaggle.com/c/generative-dog-images)

  - [Stable Diffusion - Image to Prompts](https://www.kaggle.com/competitions/stable-diffusion-image-to-prompts)

  **Time Series**

  - [OTTO – Multi-Objective Recommender System](https://www.kaggle.com/competitions/otto-recommender-system/)

  **Special**

  - [Abstraction and Reasoning Challenge](https://www.kaggle.com/c/abstraction-and-reasoning-challenge)

## Best Blog 

Very few amount of amazing blogs.

- [OpenAI](https://openai.com/research)

- [Fast-AI](https://www.fast.ai/)

- [Google AI Blog](https://ai.googleblog.com/)

- [TensorFlow Blog](https://blog.tensorflow.org/)

- [Facebook AI](https://ai.facebook.com/)

- [Berkeley AI Research](https://bair.berkeley.edu/blog/)

- **[Depth First Learning](http://www.depthfirstlearning.com/)**

- [Polo Club of Data Science](https://poloclub.github.io/)

- [OpenAI](https://openai.com/)

- [Research Blog: Stanford NLP](https://nlp.stanford.edu/blog/)

- **[PaperWithCode](https://paperswithcode.com/)**

- **[Cleverhans](http://www.cleverhans.io/)** | About: Security and privacy in machine learning.

- **[Andrej Karpathy](https://karpathy.github.io/)**

- [Visualizing ML: Jay Alammar](http://jalammar.github.io/)

- **[Lil'Log](https://lilianweng.github.io/lil-log/)**

- **[Kevin Zakka](https://kevinzakka.github.io/)**

- [Arthur Juliani](https://medium.com/@awjuliani)

- [Colah](https://colah.github.io/)

- [Michael Nielsen](http://michaelnielsen.org/)

- [Sebastian Ruder](http://ruder.io/#open)

- [PyImageSearch](https://www.pyimagesearch.com/)

- [Machine Learning Mastery](https://machinelearningmastery.com/blog/)

- [Open Source](#best-blog)

   + [Facebook Opens Source](https://opensource.facebook.com/)

   + [Google Open Source](https://opensource.google/)

- [Annotated PyTorch Paper Implementations](https://nn.labml.ai/)

## Conferences

- [CVPR - IEEE Conference on Computer Vision and Pattern Recognition](http://cvpr2018.thecvf.com/)

- [NeurIPS : Neural Information Processing Systems (NIPS)](https://nips.cc/)

- [ECCV : European Conference on Computer Vision](https://eccv2020.eu/)

- [ICML : International Conference on Machine Learning](https://icml.cc/)

- [ICCV : IEEE/CVF International Conference on Computer Vision](http://iccv2019.thecvf.com/)

## YouTube Star

- [DSA | mycodeschool](https://www.youtube.com/user/mycodeschool)

- [Arxiv Insights](https://www.youtube.com/c/ArxivInsights)

- [Welch Labs](https://www.youtube.com/c/WelchLabsVideo/featured)

- [3Blue1Brown](https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw) 

- [Edureka](https://www.youtube.com/channel/UCkw4JCwteGrDHIsyIIKo4tQ) 

- [Sentdex](https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ) 

- [Standford School | CNN for Visual Recognition](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) 

- [Code Bullet](https://www.youtube.com/channel/UC0e3QhIYukixgh5VVpKHH9Q/featured)

- [Khan Academy](https://www.youtube.com/channel/UC4a-Gbdw7vOaccHmFo40b9g)

## Contact

If 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]([email protected]) me or reach out on [LinkedIn](https://www.linkedin.com/in/innat2k14/).