Machine-Learning Tutorials
Scikit-Learn Tutorial: An Introduction to Machine Learning
- The official scikit-learn tutorial introduces supervised and unsupervised learning, model fitting, prediction, and estimator evaluation. It provides consistent examples for practicing core machine learning workflows using Python.
Scikit-Learn: A Beginner's Guide – DigitalOcean
- A structured tutorial covering real-world applications of scikit-learn. Demonstrates preprocessing, training models (classification, regression, clustering), and evaluating results using intuitive code examples.
Python Machine Learning Tutorial – DataCamp
- An end-to-end introduction to machine learning with scikit-learn. Includes hands-on examples of data loading, feature scaling, model training, and validation using well-known datasets.
TensorFlow 2 Quickstart for Beginners
- A concise, official TensorFlow guide that uses the Keras API to build and train a basic neural network. Ideal for learners new to deep learning who want to work within TensorFlow’s high-level interface.
Learning PyTorch with Examples
- A beginner-oriented PyTorch tutorial that introduces tensors, automatic differentiation, and simple neural network construction. Offers foundational examples for those entering deep learning research with PyTorch.
