scikit-learn Cookbook
5
(3 reviews total)
This course has been retired. Check out the alternatives below
-
Advance your knowledge in tech with a Packt subscription
- Instant online access to over 7,500+ books and videos
- Constantly updated with 100+ new titles each month
- Breadth and depth in over 1,000+ technologies
-
Premodel Workflow
- 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
-
Working with Linear Models
- 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
-
Building Models with Distance Metrics
- 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
-
Classifying Data with scikit-learn
- 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
-
Postmodel Workflow
- 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