Beginner-Friendly ML Tools

 




Beginner-Friendly ML Tools

  1. Teachable Machine (Google) – No-code ML tool for training image, sound, and pose recognition models.
    🔗 https://teachablemachine.withgoogle.com/
  2. Google Colab – Cloud-based Jupyter notebook for running Python-based ML models without installation.
    🔗 https://colab.research.google.com/
  3. IBM Watson Studio – Drag-and-drop AI/ML platform for building models without deep coding knowledge.
    🔗 https://www.ibm.com/cloud/watson-studio
  4. ML5.js – JavaScript library for easy ML model implementation in web projects.
    🔗 https://ml5js.org/

Programming-Based ML Tools

  1. Scikit-learn – Popular Python library for ML with simple APIs for classification, regression, and clustering.
    🔗 https://scikit-learn.org/
  2. TensorFlow & Keras – Google’s ML framework for deep learning. Keras simplifies TensorFlow usage.
    🔗 https://www.tensorflow.org/
    🔗 https://keras.io/
  3. PyTorch – Facebook’s deep learning framework, popular for research and academic use.
    🔗 https://pytorch.org/

Automated ML (AutoML) Tools

  1. Google AutoML – Google’s AI tool for training models without deep ML knowledge.
    🔗 https://cloud.google.com/automl
  2. H2O.ai – Open-source AutoML platform for data science and ML tasks.
    🔗 https://www.h2o.ai/
  3. Auto-Sklearn – Automated ML tool built on scikit-learn for hyperparameter optimization.
    🔗 https://automl.github.io/auto-sklearn/

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