Course: Python for Machine Learning

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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

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Name		Name	Last commit message	Last commit date
Latest commit History 51 Commits
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1. Introduction.ipynb		1. Introduction.ipynb
2. Linear_Regression.ipynb		2. Linear_Regression.ipynb
3. Neural Network.ipynb		3. Neural Network.ipynb
4. Overfitting-Underfitting.ipynb		4. Overfitting-Underfitting.ipynb
5. Experiment on Real_Data.ipynb		5. Experiment on Real_Data.ipynb
6. Closing remarks.ipynb		6. Closing remarks.ipynb
README.md		README.md

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