parameters predictions with regression strengths and weaknesses kaggle kernelized support vector machines (svmskernel trick linear models and nonlinear features vs linear support vector machines mathematics of parameters predictions with preprocessing data for strengths and weaknesses tuning svm parameters understanding knn object regularization regularization lasso model latent dirichlet allocation (lda) - leafs leakage learn from the past approach learning_rate parameter leave-one-out cross-validation lemmatization - linear functions linear models classification data representation and - vs -nearest neighbors lasso linear svms logistic regression multiclass classification ordinary least squares parameters predictions with regression ridge regression strengths and weaknesses linear regression - linear support vector machines (svms) linkage arrays live testing log function loss functions low-dimensional datasets machine learning algorithm chains and pipelines - applications for - approach to problem solving - benefits of python for building your own systemsvii data representation - examples of - mathematics ofvii model evaluation and improvement - preprocessing and scaling - prerequisites to learningvii resourcesix - scikit-learn and - supervised learning - understanding your data unsupervised learning - working with text data - make_pipeline function accessing step attributes displaying steps attribute grid-searched pipelines and syntax for manifold learning algorithms applications for example of results of visualizations with mathematical functions for feature transformations matplotlib max_features parameter meta-estimators for trees and forests method chaining metrics (see evaluation metrics and scoringmglearn mllib model-based feature selection models (see also algorithmscalibrated capable of generalization coefficients with text data - complexity vs dataset size index