Notes - machine learning

  1. Week 1
    1. Giving Computers the Ability to Learn from Data [Notebook] [Zip]
    2. Training Machine Learning Algorithms for Classification [Notebook] [Zip]
  2. Week 2
    1. A Tour of Machine Learning Classifiers Using Scikit-learn [Notebook] [Zip]
    2. Building Good Training Sets – Data Preprocessing [Notebook] [Zip]
  3. Week 3
    1. Compressing Data via Dimensionality Reduction [Notebook] [Zip]
    2. Learning Best Practices for Model Eva. and Hyperparameter Tuning [Notebook]
  4. Week 4
    1. Combining Different Models for Ensemble Learning [Notebook]
    2. Applying Machine Learning to Sentiment Analysis [Notebook]
  5. Week 5
    1. Embedding a Machine Learning Model into a Web Application [Notebook]
    2. Predicting Continuous Target Variables with Regression Analysis [Notebook]
  6. Week 6
    1. Working with Unlabeled Data – Clustering Analysis [Notebook]
    2. Training Artificial Neural Networks for Image Recognition [Notebook]
  7. Week 7
    1. Parallelizing Neural Network Training with Theano [Notebook]
    2. Thinking in Machine Learning [Notebook]
  8. Week 8
    1. Tools and Techniques [Notebook]
    2. Turning Data into Information [Notebook]
  9. Week 9
    1. Models – Learning from Information [Notebook]
    2. Linear Models [Notebook]
  10. Week 10
    1. Neural Networks [Notebook]
    2. Features – How Algorithms See the World [Notebook]
  11. Week 11
    1. Learning with Ensembles [Notebook]
    2. Design Strategies and Case Studies [Notebook]
  12. Week 12
    1. Unsupervised Machine Learning [Notebook]
    2. Deep Belief Networks [Notebook]
  13. Week 13
    1. Stacked Denoising Autoencoders [Notebook]
    2. Convolutional Neural Networks [Notebook]
  14. Week 14
    1. Semi-Supervised Learning [Notebook]
    2. Text Feature Engineering [Notebook]
    3. Ensemble Methods [Notebook]
    4. Additional Python Machine Learning Tools [Notebook]