Y. Emre Yildiz

Notes - machine learning

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

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