“The Best Public Datasets for Machine Learning and Data Science” by Stacy Stanford

https://datasimplifier.com/best-data-analyst-projects-for-freshers/

https://toolbox.google.com/datasetsearch

https://www.kaggle.com/datasets

https://mlr.cs.umass.edu/ml/

https://www.visualdata.io/

https://guides.library.cmu.edu/machine-learning/datasets

https://www.data.gov/

https://nces.ed.gov/

https://www.ukdataservice.ac.uk/

https://datausa.io/

https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html

https://www.kaggle.com/xiuchengwang/python-dataset-download

https://www.quandl.com/

https://data.worldbank.org/

https://www.imf.org/en/Data

https://markets.ft.com/data/

https://trends.google.com/trends/?q=google&ctab=0&geo=all&date=all&sort=0

https://www.aeaweb.org/resources/data/us-macro-regional

https://xviewdataset.org/#dataset

https://labelme.csail.mit.edu/Release3.0/browserTools/php/dataset.php

https://image-net.org/

https://cocodataset.org/

https://visualgenome.org/

https://ai.googleblog.com/2016/09/introducing-open-images-dataset.html?m=1

https://vis-www.cs.umass.edu/lfw/

https://vision.stanford.edu/aditya86/ImageNetDogs/

https://web.mit.edu/torralba/www/indoor.html

https://www.cs.jhu.edu/~mdredze/datasets/sentiment/

https://ai.stanford.edu/~amaas/data/sentiment/

https://nlp.stanford.edu/sentiment/code.html

https://help.sentiment140.com/for-students/

https://www.kaggle.com/crowdflower/twitter-airline-sentiment

https://hotpotqa.github.io/

https://www.cs.cmu.edu/~./enron/

https://snap.stanford.edu/data/web-Amazon.html

https://aws.amazon.com/datasets/google-books-ngrams/

https://u.cs.biu.ac.il/~koppel/BlogCorpus.htm

https://code.google.com/archive/p/wiki-links/downloads

https://www.dt.fee.unicamp.br/~tiago/smsspamcollection/

https://www.yelp.com/dataset

https://t.iss.one/DataPortfolio/2

https://archive.ics.uci.edu/ml/datasets/Spambase

https://bdd-data.berkeley.edu/

https://apolloscape.auto/

https://archive.org/details/comma-dataset

https://www.cityscapes-dataset.com/

https://aplicaciones.cimat.mx/Personal/jbhayet/ccsad-dataset

https://www.vision.ee.ethz.ch/~timofter/traffic_signs/

https://cvrr.ucsd.edu/LISA/datasets.html

https://hci.iwr.uni-heidelberg.de/node/6132

https://www.lara.prd.fr/benchmarks/trafficlightsrecognition

https://computing.wpi.edu/dataset.html

https://mimic.physionet.org/

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Essential Tools, Libraries, and Frameworks to learn Artificial Intelligence

1. Programming Languages:

Python

R

Java

Julia


2. AI Frameworks:

TensorFlow

PyTorch

Keras

MXNet

Caffe


3. Machine Learning Libraries:

Scikit-learn: For classical machine learning models.

XGBoost: For boosting algorithms.

LightGBM: For gradient boosting models.


4. Deep Learning Tools:

TensorFlow

PyTorch

Keras

Theano


5. Natural Language Processing (NLP) Tools:

NLTK (Natural Language Toolkit)

SpaCy

Hugging Face Transformers

Gensim


6. Computer Vision Libraries:

OpenCV

DLIB

Detectron2


7. Reinforcement Learning Frameworks:

Stable-Baselines3

RLlib

OpenAI Gym


8. AI Development Platforms:

IBM Watson

Google AI Platform

Microsoft AI


9. Data Visualization Tools:

Matplotlib

Seaborn

Plotly

Tableau


10. Robotics Frameworks:

ROS (Robot Operating System)

MoveIt!


11. Big Data Tools for AI:

Apache Spark

Hadoop


12. Cloud Platforms for AI Deployment:

Google Cloud AI

AWS SageMaker

Microsoft Azure AI


13. Popular AI APIs and Services:

Google Cloud Vision API

Microsoft Azure Cognitive Services

IBM Watson AI APIs


14. Learning Resources and Communities:

Kaggle

GitHub AI Projects

Papers with Code


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