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tutorials
machine_learning
Notebook
Definitions and Advices
1 What is Machine Learning ?
1.1 Documentation and reference:
2 Supervised and Unsupervised Learning
3 Cheat Sheet - scikit-learn Algorithm Selection
4 Machine Learning Wisdom
Prepare the Data
1 Datasets available in scikit-learn
1.1 Example: the "Iris" Packaged Dataset
1.2 Example: the "Digits" Packaged Dataset
1.3 Example: the "S-Curve" Generated Dataset
2 Using pandas
3 Working with MATLAB files
4 Preprocessing Data
4.1 Standardizing = Mean Removal + Variance Scaling:
4.2 Using the preprocessing.StandardScaler with pandas:
4.3 Normalizing = Dividing by a Norm of the Vector:
5 Features Extraction
5.1 Derived Features:
5.2 DictVectorizer uses "one-hot" encoder for categorical features:
5.3 The Bag of Words representation:
The scikit-learn interface
1 Simple estimator example: fit a linear regression model
2 Separate Training and Validation Sets
2.1 Example: Do a Regression Analysis on MATLAB
®
data
2.2 Example: Training and a Validation Sets on a Classification Problem
3 Cross Validation (CV)
3.1 Cross Validation: test many estimators on the same dataset:
3.2 Cross Validation: test many hyperparamaters and estimators on the same dataset:
3.3 Model specific Cross Validation:
3.4 Cross-validation iterators
4 Grid Search: Searching for estimator hyperparameters
4.1 Exhaustive Grid Search
4.2 Randomized Parameter Optimization
Visualizing the Data
1 Principal Component Analysis (PCA)
2 Linear Discriminant Alanysis (LDA)
3 Manifold Learning
3.1 Another example un a specific test dataset: the S-Curve
3.2 Some Tips on Manifold Learning practical use
4 Totally Random Trees Embedding