Learn how machines learn from data. Build intelligent systems that automate decisions using supervised and unsupervised learning techniques.
Math, linear algebra basics, and probability for ML.
Decision trees, SVMs, and ensemble methods.
Serve models with APIs and monitor performance in production.
Scikit-learn, Jupyter, MLflow basics, and GitHub for versioning.
Build recommendation systems, churn prediction, and anomaly detection.
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