You don't need to buy a GPU for machine learning work!

There are other alternatives. Here are some:

1. Google Colab
2. Kaggle
3. Deepnote
4. AWS SageMaker
5. GCP Notebooks
6. Azure Notebooks
7. Cocalc
8. Binder
9. Saturncloud
10. Datablore
11. IBM Notebooks
12. Ola kutrim

Spend your time focusing on your problem.💪💪

95% of Machine Learning solutions in the real world are for tabular data.

Not LLMs, not transformers, not agents, not fancy stuff.

Learning to do feature engineering and build tree-based models will open a ton of opportunities.

“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


Share with credits: https://t.iss.one/machinelearning_deeplearning

ENJOY LEARNING 👍👍