scikit-learn, TensorFlow & Keras

The core ML stack on Deepnote: scikit-learn for classical ML, TensorFlow / Keras for deep learning. Each notebook below opens directly in Deepnote with one click.

Applied projects

• Detect Credit Card Fraud — End-to-end scikit-learn classifier on the canonical Kaggle fraud dataset. Imbalanced-class handling included. → Launch in Deepnote

• Customer Churn Prediction — Classical ML pipeline (feature engineering → model selection → evaluation) on a telecom churn dataset. → Launch in Deepnote

Books with end-to-end notebooks

• Hands-on Machine Learning with Scikit-Learn and TensorFlow — Aurélien Géron's reference text. Every chapter ships a runnable notebook from ageron/handson-ml. → Launch chapter 2 (end-to-end project)

• Deep Learning with TensorFlow 2 and Keras — Companion course material. Practical, focused on TF2 idioms.

See also

• Books & journals — longer-form curricula
• TensorBoard — visualizing training runs from these notebooks