Machine-Learning-By-Example

Using Python, NumPy, SciPy, and Pandas to build machine learning algorithms for regression and classification. The data files are from Andrew Ng's Coursera course on machine learning. Work in this repo is ongoing with content changes and additions coming fast and furious. Stay tuned!

Name		Name	Last commit message	Last commit date
Latest commit History 13 Commits
Articles		Articles
Data		Data
Images		Images
.gitignore		.gitignore
Chapter-1-Think-Like-A-Machine.ipynb		Chapter-1-Think-Like-A-Machine.ipynb
Chapter-2-Regression.ipynb		Chapter-2-Regression.ipynb
Chapter-3-Regression-With-Many-Inputs.ipynb		Chapter-3-Regression-With-Many-Inputs.ipynb
Chapter-4-Non-Linear-Regression.ipynb		Chapter-4-Non-Linear-Regression.ipynb
Chapter-5-Logistic Regression.ipynb		Chapter-5-Logistic Regression.ipynb
Chapter-6-Non-Linear-Logistic-Regression.ipynb		Chapter-6-Non-Linear-Logistic-Regression.ipynb
Chapter-7-Neural-Networks.ipynb		Chapter-7-Neural-Networks.ipynb
Course-Outline-Machine-Learning.ipynb		Course-Outline-Machine-Learning.ipynb
Course-Outline-Statistical-Inference.ipynb		Course-Outline-Statistical-Inference.ipynb
LICENSE		LICENSE
Learn Like a Machine v5.pdf		Learn Like a Machine v5.pdf
Learn Like a Machine v5.pptx		Learn Like a Machine v5.pptx
README.md		README.md
SharedFunctions.ipynb		SharedFunctions.ipynb
mnist_loader.py		mnist_loader.py
mnist_loader.pyc		mnist_loader.pyc
plot_mnist_filters.ipynb		plot_mnist_filters.ipynb
scatter3d_demo.py		scatter3d_demo.py

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