datascience

Add Jupyter notebooks to the corresponding folder.
To maintain the order, create one notebook per module. e.g. 2-1-this-is-module-1-for-chapter-2, 3-3-this-is-module-3-for-chapter-3.
If you need to add extra info, add it to the README file inside each folder.
Use scikit-learn, numpy and Pandas only.

Name		Name	Last commit message	Last commit date
Latest commit History 70 Commits
1-Introduction-to-machine-learning-software-stack/1.3		1-Introduction-to-machine-learning-software-stack/1.3
2-intro-to-ml		2-intro-to-ml
3-project-overview		3-project-overview
4-regression		4-regression
5-classification		5-classification
6-clustering		6-clustering
7---practical-methodologies		7---practical-methodologies
datasets		datasets
examples		examples
images		images
kaggle-data		kaggle-data
.gitignore		.gitignore
7 - Practical Methodologies		7 - Practical Methodologies
Error Analysis and Classification Measures.ipynb		Error Analysis and Classification Measures.ipynb
Iris PCA.ipynb		Iris PCA.ipynb
LICENSE		LICENSE
Learning curves and bias-variance tradeoff.ipynb		Learning curves and bias-variance tradeoff.ipynb
Ng's Machine Learning Exercise 6.ipynb		Ng's Machine Learning Exercise 6.ipynb
README.md		README.md
evaluating estimator performance using cross-validation.ipynb		evaluating estimator performance using cross-validation.ipynb
requirements.txt		requirements.txt

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