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).