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scikit-learn Recipes

Video Introducing this tutorial


Data Pre-Processing with scikit-learn :
The Course Overview
Loading Data
Building Binary Features by Creating Thresholds
Imputing Missing Values Using sklearn Impute
Building Linear Model with Outliers
Putting It All Together with sklearn Pipelines

Dimensionality Reduction :
Principal Components Analysis
t-SNE
Factor Analysis
Kernel PCA

Linear Models :
Linear Regression without scikit-learn
Linear Regression with scikit-learn
Evaluating the Linear Regression Model
Logistic Regression
Evaluating the Logistic Regression Model

Support Vector Machines :
Linear SVM
Optimizing Linear SVM
Multiclass Classification Using Consumer Complaints Data

Decision Trees and Ensembles :
Decision Trees
Decision Tree Model Evaluation and Fine Tuning
Building a Random Forest Regressor
k-Nearest-Neighbor Model
Gradient Boosting

Clustering with scikit-learn :
Clustering Data with k-means
Evaluating the Performance of the Model
Fine-Tuning the k-means Model
Detecting Outlier Using k-means Clustering
Gaussian Mixture Models for Variable Clustering

Cross-Validation :
Introduction to Feature Selection
Hands-On Feature Selection
k-fold Cross-Validation
ShuffleSplit and Time Series Cross-Validation
L1 and L2 Norms
Grid Search

Neural Networks :
Introduction to Neural Networks
Building a Perceptron Classifier
Multilayer Perceptron with scikit-learn
Stacking