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Course materials on Python for machine learning:
- Introduction
- Basics of machine learning
- AI-ML-DL
- Types of ML techniques
- Python essential libraries for ML
- Linear and Nonlinear Reggression
- Implementation using Scikit-learn
- Neural-Network
- Implementation using Keras
- ML best practices to remember
- Train_Test_split
- Overfitting_Unerfitting
- Bias-Variance-Tradeoff
- Experiment on real data (ML application for climate modelling)
- Closing remarks