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