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A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python

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Name		Name	Last commit message	Last commit date
Latest commit History 270 Commits
datasets		datasets
images		images
models		models
tutorials		tutorials
.gitignore		.gitignore
LICENSE		LICENSE
NOTES.md		NOTES.md
README.md		README.md
classification.ipynb		classification.ipynb
convolutional-neural-networks.ipynb		convolutional-neural-networks.ipynb
decision-trees.ipynb		decision-trees.ipynb
deep-learning.ipynb		deep-learning.ipynb
dimensionality-reduction.ipynb		dimensionality-reduction.ipynb
distributed-tensorflow.ipynb		distributed-tensorflow.ipynb
ensemble-learning-and-random-forests.ipynb		ensemble-learning-and-random-forests.ipynb
housing.ipynb		housing.ipynb
introduction-to-artificial-neural-networks.ipynb		introduction-to-artificial-neural-networks.ipynb
lifesat.ipynb		lifesat.ipynb
mnist-cnn.py		mnist-cnn.py
recurrent-neural-networks.ipynb		recurrent-neural-networks.ipynb
spam-filter.ipynb		spam-filter.ipynb
support-vector-machines.ipynb		support-vector-machines.ipynb
tensorflow_graph_in_jupyter.py		tensorflow_graph_in_jupyter.py
titanic.ipynb		titanic.ipynb
training-linear-models.ipynb		training-linear-models.ipynb
up-and-running-with-tensorflow.ipynb		up-and-running-with-tensorflow.ipynb

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Machine Learning Notebooks

Repository contains the example code with the fundamentals of Machine Learning in python with Scikit-Learn and TensorFlow.

Tutorials:

Kaggle

Notebooks:

The Fundamentals of Machine Learning:
- Lifesat - predict life satisfaction based on the GDP per capita.
- Housing - predict median house values in Californian districts, given a number of features from these districts.
- Classification - classification of the MNIST handwritten digits.
- Titanic - complete the analysis of what sorts of people were likely to survive.
- Spam Classifier - build a spam classifier.
- Training Linear Models - learn how to train linear models.
- Support Vector Machines - learn about support vector machines.
- Decision Trees - learn how to train, visualize, and make predictions with Decision Trees.
- Ensemble Learning and Random Forests - discover the most popular Ensemble methods, including bagging, boosting, stacking, Random Forests...
- Dimensionality Reduction - learn more about reduce dimensionality technique.
Neural Networks and Deep Learning:
- Up and running with TensorFlow - learn more about powerful open source software library for numerical computation.
- Introduction to Artificial Neural Networks - learn more about artificial neural networks (ANNs).
- Training Deep Neural Nets - learn how to train deep DNNs.
- Distributed TensorFlow - learn how to how to use TensorFlow to distribute computations across multiple devices (CPUs and GPUs) and run them in parallel.
- Convolutional Neural Networks - learn more about CNNs.
- Recurrent Neural Networks - learn more about recurrent neural netwprks (RNN).