What you need for this learning path
by Gavin Hackeling, Trent Hauck, Guillermo Moncecchi, Raúl Garreta
scikit-learn : Machine Learning Simplified
- scikit-learn: Machine Learning Simplified
- Table of Contents
- Credits
- Preface
- 1. Module 1
- 2. Module 2
- 1. Premodel Workflow
- Introduction
- Getting sample data from external sources
- Creating sample data for toy analysis
- Scaling data to the standard normal
- Creating binary features through thresholding
- Working with categorical variables
- Binarizing label features
- Imputing missing values through various strategies
- Using Pipelines for multiple preprocessing steps
- Reducing dimensionality with PCA
- Using factor analysis for decomposition
- Kernel PCA for nonlinear dimensionality reduction
- Using truncated SVD to reduce dimensionality
- Decomposition to classify with DictionaryLearning
- Putting it all together with Pipelines
- Using Gaussian processes for regression
- Defining the Gaussian process object directly
- Using stochastic gradient descent for regression
- 2. Working with Linear Models
- Introduction
- Fitting a line through data
- Evaluating the linear regression model
- Using ridge regression to overcome linear regression's shortfalls
- Optimizing the ridge regression parameter
- Using sparsity to regularize models
- Taking a more fundamental approach to regularization with LARS
- Using linear methods for classification – logistic regression
- Directly applying Bayesian ridge regression
- Using boosting to learn from errors
- 3. Building Models with Distance Metrics
- Introduction
- Using KMeans to cluster data
- Optimizing the number of centroids
- Assessing cluster correctness
- Using MiniBatch KMeans to handle more data
- Quantizing an image with KMeans clustering
- Finding the closest objects in the feature space
- Probabilistic clustering with Gaussian Mixture Models
- Using KMeans for outlier detection
- Using k-NN for regression
- 4. Classifying Data with scikit-learn
- Introduction
- Doing basic classifications with Decision Trees
- Tuning a Decision Tree model
- Using many Decision Trees – random forests
- Tuning a random forest model
- Classifying data with support vector machines
- Generalizing with multiclass classification
- Using LDA for classification
- Working with QDA – a nonlinear LDA
- Using Stochastic Gradient Descent for classification
- Classifying documents with Naïve Bayes
- Label propagation with semi-supervised learning
- 5. Postmodel Workflow
- Introduction
- K-fold cross validation
- Automatic cross validation
- Cross validation with ShuffleSplit
- Stratified k-fold
- Poor man's grid search
- Brute force grid search
- Using dummy estimators to compare results
- Regression model evaluation
- Feature selection
- Feature selection on L1 norms
- Persisting models with joblib
- 1. Premodel Workflow
- 3. Module 3
- 1. The Fundamentals of Machine Learning
- 2. Linear Regression
- 3. Feature Extraction and Preprocessing
- 4. From Linear Regression to Logistic Regression
- 5. Nonlinear Classification and Regression with Decision Trees
- 6. Clustering with K-Means
- 7. Dimensionality Reduction with PCA
- 8. The Perceptron
- 9. From the Perceptron to Support Vector Machines
- 10. From the Perceptron to Artificial Neural Networks
- Bibliography
- Index
Module 1:
For running the module's examples, you will need a running Python environment, including the scikit-learn library and NumPy and SciPy mathematical libraries. The source code will be available in the form of IPython notebooks. For Chapter 4, Advanced Features, we will also include the Pandas Python library. Chapter 1, Machine Learning – A Gentle Introduction, shows how to install them in your operating system.
Module 2:
Here are the contents that will get the environment set up. This will allow you to follow along with the code in this module. This method may be easier for less-experienced Python developers:
dateutil==2.1
ipython==2.2.0
ipython-notebook==2.1.0
jinja2==2.7.3
markupsafe==0.18
matplotlib==1.3.1
numpy==1.8.1
patsy==0.3.0
pandas==0.14.1
pip==1.5.6
pydot==1.0.28
pyparsing==1.5.6
pytz==2014.4
pyzmq==14.3.1
scikit-learn==0.15.0
scipy==0.14.0
setuptools==3.6
six==1.7.3
ssl_match_hostname==3.4.0.2
tornado==3.2.2
Module 3:
The examples in this module assume that you have an installation of Python 2.7. The first chapter will describe methods to install scikit-learn 0.15.2, its dependencies, and other libraries on Linux, OS X, and Windows.
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