sklearn-free-tutorials

Practice scikit-learn Free Tutorials | This repo collects 294 of free tutorials for scikit-learn. scikit-learn is a powerful machine learning library for Python. This Skill Tree offers a comprehensive learning path for mastering scikit-learn. Ideal for data science beginners, it provides a struct...
https://github.com/labex-labs/sklearn-free-tutorials

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- 📖 Linear Models in Scikit-Learn - linear-models-in-scikit-learn-71093) |
- 📖 Discriminant Analysis Classifiers Explained - discriminant-analysis-classifiers-explained-71094) |
- 📖 Exploring Scikit-Learn Datasets and Estimators - exploring-scikit-learn-datasets-and-estimators-71095) |
- 📖 Kernel Ridge Regression - kernel-ridge-regression-71096) |
- 📖 Supervised Learning with Scikit-Learn - supervised-learning-with-scikit-learn-71097) |
- 📖 Species Distribution Modeling - species-distribution-modeling-49298) |
- 📖 Manifold Learning on Spherical Data - manifold-learning-on-spherical-data-49208) |
- 📖 Map Data to a Normal Distribution - map-data-to-a-normal-distribution-49209) |
- 📖 Visualize High-Dimensional Data with MDS - visualize-high-dimensional-data-with-mds-49210) |
- 📖 Mean-Shift Clustering Algorithm - mean-shift-clustering-algorithm-49211) |
- 📖 Comparing K-Means and MiniBatchKMeans - comparing-k-means-and-minibatchkmeans-49212) |
- 📖 Impute Missing Data - impute-missing-data-49213) |
- 📖 Multi-Layer Perceptron Regularization - multi-layer-perceptron-regularization-49214) |
- 📖 Ensemble Methods Exploration with Scikit-Learn - ensemble-methods-exploration-with-scikit-learn-71108) |
- 📖 Multiclass and Multioutput Algorithms - multiclass-and-multioutput-algorithms-71109) |
- 📖 Feature Selection with Scikit-Learn - feature-selection-with-scikit-learn-71110) |
- 📖 Scikit-Learn MLPClassifier: Stochastic Learning Strategies - scikit-learn-mlpclassifier-stochastic-learning-strategies-49215) |
- 📖 Classify Handwritten Digits with MLP Classifier - classify-handwritten-digits-with-mlp-classifier-49216) |
- 📖 Gradient Boosting Monotonic Constraints - gradient-boosting-monotonic-constraints-49218) |
- 📖 Optimizing Model Hyperparameters with GridSearchCV - optimizing-model-hyperparameters-with-gridsearchcv-49219) |
- 📖 Joint Feature Selection with Multi-Task Lasso - joint-feature-selection-with-multi-task-lasso-49220) |
- 📖 Multi-Label Document Classification - multi-label-document-classification-49221) |
- 📖 Plot Nca Classification - plot-nca-classification-49223) |
- 📖 Neighborhood Components Analysis - neighborhood-components-analysis-49225) |
- 📖 Nearest Centroid Classification - nearest-centroid-classification-49226) |
- 📖 Nested Cross-Validation for Model Selection - nested-cross-validation-for-model-selection-49227) |
- 📖 Non-Negative Least Squares Regression - non-negative-least-squares-regression-49228) |
- 📖 Linear Regression Example - linear-regression-example-49231) |
- 📖 Sparse Signal Recovery with Orthogonal Matching Pursuit - sparse-signal-recovery-with-orthogonal-matching-pursuit-49232) |
- 📖 OPTICS Clustering Algorithm - optics-clustering-algorithm-49234) |
- 📖 Text Classification Using Out-of-Core Learning - text-classification-using-out-of-core-learning-49235) |
- 📖 Outlier Detection Using Scikit-Learn Algorithms - outlier-detection-using-scikit-learn-algorithms-49236) |
- 📖 Detecting Outliers in Wine Data - detecting-outliers-in-wine-data-49237) |
- 📖 Principal Components Analysis - principal-components-analysis-49239) |
- 📖 Plot Pca vs Fa Model Selection - plot-pca-vs-fa-model-selection-49241) |
- 📖 Plot Pca vs Lda - plot-pca-vs-lda-49242) |
- 📖 Plot PCR vs PLS - plot-pcr-vs-pls-49243) |
- 📖 Permutation Test Score for Classification - permutation-test-score-for-classification-49246) |
- 📖 Constructing Scikit-Learn Pipelines - constructing-scikit-learn-pipelines-49247) |
- 📖 Precision-Recall Metric for Imbalanced Classification - precision-recall-metric-for-imbalanced-classification-49249) |
- 📖 Quantile Regression with Scikit-Learn - quantile-regression-with-scikit-learn-49251) |
- 📖 Random Classification Dataset Plotting - random-classification-dataset-plotting-49252) |
- 📖 Hashing Feature Transformation - hashing-feature-transformation-49253) |
- 📖 Plot Random Forest Regression Multioutput - plot-random-forest-regression-multioutput-49254) |
- 📖 Multilabel Dataset Generation with Scikit-Learn - multilabel-dataset-generation-with-scikit-learn-49255) |
- 📖 Model-Based and Sequential Feature Selection - model-based-and-sequential-feature-selection-49279) |
- 📖 Hyperparameter Optimization: Randomized Search vs Grid Search - hyperparameter-optimization-randomized-search-vs-grid-search-49256) |
- 📖 Digit Classification with RBM Features - digit-classification-with-rbm-features-49259) |
- 📖 Effect of Varying Threshold for Self-Training - effect-of-varying-threshold-for-self-training-49280) |
- 📖 Recursive Feature Elimination - recursive-feature-elimination-49267) |
- 📖 Recursive Feature Elimination with Cross-Validation - recursive-feature-elimination-with-cross-validation-49268) |
- 📖 Ridge Regression for Linear Modeling - ridge-regression-for-linear-modeling-49269) |
- 📖 Robust Linear Estimator Fitting - robust-linear-estimator-fitting-49271) |
- 📖 Robust Covariance Estimation in Python - robust-covariance-estimation-in-python-49272) |
- 📖 ROC with Cross Validation - roc-with-cross-validation-49273) |
- 📖 Scikit-Learn Visualization API - scikit-learn-visualization-api-49274) |
- 📖 Multiclass ROC Evaluation with Scikit-Learn - multiclass-roc-evaluation-with-scikit-learn-49275) |
- 📖 Polynomial Kernel Approximation with Scikit-Learn - polynomial-kernel-approximation-with-scikit-learn-49276) |
- 📖 Feature Scaling in Machine Learning - feature-scaling-in-machine-learning-49277) |
- 📖 Spectral Clustering for Image Segmentation - spectral-clustering-for-image-segmentation-49278) |
- 📖 Semi-Supervised Text Classification - semi-supervised-text-classification-49281) |
- 📖 Semi-Supervised Classifiers on the Iris Dataset - semi-supervised-classifiers-on-the-iris-dataset-49282) |
- 📖 SVM for Unbalanced Classes - svm-for-unbalanced-classes-49283) |
- 📖 SVM: Maximum Margin Separating Hyperplane - svm-maximum-margin-separating-hyperplane-49284) |
- 📖 Using Set_output API - using-set-output-api-49285) |
- 📖 Comparing Online Solvers for Handwritten Digit Classification - comparing-online-solvers-for-handwritten-digit-classification-49286) |
- 📖 Early Stopping of Stochastic Gradient Descent - early-stopping-of-stochastic-gradient-descent-49287) |
- 📖 Scikit-Learn Multi-Class SGD Classifier - scikit-learn-multi-class-sgd-classifier-49288) |
- 📖 Applying Regularization Techniques with SGD - applying-regularization-techniques-with-sgd-49290) |
- 📖 Naive Bayes Example - naive-bayes-example-71106) |
- 📖 Decision Tree Classification with Scikit-Learn - decision-tree-classification-with-scikit-learn-71107) |
- 📖 Scikit-Learn Iterative Imputer - scikit-learn-iterative-imputer-49173) |
- 📖 Plot SGD Separating Hyperplane - plot-sgd-separating-hyperplane-49291) |
- 📖 Plot Sgdocsvm vs Ocsvm - plot-sgdocsvm-vs-ocsvm-49293) |
- 📖 Sparse Coding with Precomputed Dictionary - sparse-coding-with-precomputed-dictionary-49294) |
- 📖 Decomposing Signals in Components - decomposing-signals-in-components-71118) |
- 📖 Exploring Scikit-Learn SGD Classifiers - exploring-scikit-learn-sgd-classifiers-71100) |
- 📖 Implementing Stochastic Gradient Descent - implementing-stochastic-gradient-descent-71102) |
- 📖 Gaussian Process Regression and Classification - gaussian-process-regression-and-classification-71104) |
- 📖 Exploring Johnson-Lindenstrauss Lemma with Random Projections - exploring-johnson-lindenstrauss-lemma-with-random-projections-49174) |
- 📖 Simple 1D Kernel Density Estimation - simple-1d-kernel-density-estimation-49175) |
- 📖 Explicit Feature Map Approximation for RBF Kernels - explicit-feature-map-approximation-for-rbf-kernels-49176) |
- 📖 Principal Component Analysis with Kernel PCA - principal-component-analysis-with-kernel-pca-49177) |
- 📖 Plot Kernel Ridge Regression - plot-kernel-ridge-regression-49178) |
- 📖 Exploring K-Means Clustering Assumptions - exploring-k-means-clustering-assumptions-49179) |
- 📖 K-Means Clustering on Handwritten Digits - k-means-clustering-on-handwritten-digits-49180) |
- 📖 Clustering Analysis with Silhouette Method - clustering-analysis-with-silhouette-method-49182) |
- 📖 Empirical Evaluation of K-Means Initialization - empirical-evaluation-of-k-means-initialization-49183) |
- 📖 Active Learning Withel Propagation - active-learning-withel-propagation-49184) |
- 📖 Label Propagation Learning - label-propagation-learning-49186) |
- 📖 Sparse Signal Regression with L1-Based Models - sparse-signal-regression-with-l1-based-models-49187) |
- 📖 Lasso and Elastic Net - lasso-and-elastic-net-49188) |
- 📖 Scikit-Learn Lasso Regression - scikit-learn-lasso-regression-49189) |
- 📖 Semi-Supervised Learning Algorithms - semi-supervised-learning-algorithms-71111) |
- 📖 Nonlinear Regression with Isotonic - nonlinear-regression-with-isotonic-71112) |
- 📖 Neural Network Models - neural-network-models-71113) |
- 📖 Gaussian Mixture Models - gaussian-mixture-models-71114) |
- 📖 Manifold Learning with Scikit-Learn - manifold-learning-with-scikit-learn-71115) |
- 📖 Unsupervised Clustering with K-Means - unsupervised-clustering-with-k-means-71116) |
- 📖 Biclustering in Scikit-Learn - biclustering-in-scikit-learn-71117) |
- 📖 Permutation Feature Importance - permutation-feature-importance-71127) |
- 📖 Transforming the Prediction Target - transforming-the-prediction-target-71136) |
- 📖 Pairwise Metrics and Kernels in Scikit-Learn - pairwise-metrics-and-kernels-in-scikit-learn-71135) |
- 📖 Kernel Approximation Techniques in Scikit-Learn - kernel-approximation-techniques-in-scikit-learn-71134) |
- 📖 Imputation of Missing Values - imputation-of-missing-values-71131) |
- 📖 Preprocessing Techniques in Scikit-Learn - preprocessing-techniques-in-scikit-learn-71130) |
- 📖 Feature Extraction with Scikit-Learn - feature-extraction-with-scikit-learn-71129) |
- 📖 Pipelines and Composite Estimators - pipelines-and-composite-estimators-71128) |
- 📖 Tuning Hyperparameters of an Estimator - tuning-hyperparameters-of-an-estimator-71123) |
- 📖 Covariance Matrix Estimation with Scikit-Learn - covariance-matrix-estimation-with-scikit-learn-71119) |
- 📖 Density Estimation Using Kernel Density - density-estimation-using-kernel-density-71121) |
- 📖 Machine Learning Cross-Validation with Python - machine-learning-cross-validation-with-python-71122) |
- 📖 Validation Curves: Plotting Scores to Evaluate Models - validation-curves-plotting-scores-to-evaluate-models-71125) |
- 📖 Partial Dependence and Individual Conditional Expectation - partial-dependence-and-individual-conditional-expectation-71126) |
- 📖 Model Selection: Choosing Estimators and Their Parameters - model-selection-choosing-estimators-and-their-parameters-71098) |
- 📖 Incremental Principal Component Analysis on Iris Dataset - incremental-principal-component-analysis-on-iris-dataset-49164) |
- 📖 Iris Flower Classification with Scikit-learn - iris-flower-classification-with-scikit-learn-49166) |
- 📖 Decision Trees on Iris Dataset - decision-trees-on-iris-dataset-49167) |
- 📖 Logistic Regression Classifier on Iris Dataset - logistic-regression-classifier-on-iris-dataset-49169) |
- 📖 SVM Classifier on Iris Dataset - svm-classifier-on-iris-dataset-49170) |
- 📖 Anomaly Detection with Isolation Forest - anomaly-detection-with-isolation-forest-49171) |
- 📖 Nonparametric Isotonic Regression with Scikit-Learn - nonparametric-isotonic-regression-with-scikit-learn-49172) |
- 📖 Lasso Model Selection - lasso-model-selection-49190) |
- 📖 Model Selection for Lasso Regression - model-selection-for-lasso-regression-49192) |
- 📖 Discriminant Analysis Classification Algorithms - discriminant-analysis-classification-algorithms-49193) |
- 📖 Linear Discriminant Analysis for Classification - linear-discriminant-analysis-for-classification-49194) |
- 📖 Plotting Learning Curves - plotting-learning-curves-49195) |
- 📖 Class Likelihood Ratios to Measure Classification Performance - class-likelihood-ratios-to-measure-classification-performance-49196) |
- 📖 Hierarchical Clustering with Scikit-Learn - hierarchical-clustering-with-scikit-learn-49198) |
- 📖 Spectral Biclustering Algorithm - spectral-biclustering-algorithm-49300) |
- 📖 Spectral Co-Clustering Algorithm - spectral-co-clustering-algorithm-49301) |
- 📖 Manifold Learning on Handwritten Digits - manifold-learning-on-handwritten-digits-49199) |
- 📖 Local Outlier Factor for Novelty Detection - local-outlier-factor-for-novelty-detection-49200) |
- 📖 Outlier Detection with LOF - outlier-detection-with-lof-49201) |
- 📖 Step-by-Step Logistic Regression - step-by-step-logistic-regression-49202) |
- 📖 Plot Multinomial and One-vs-Rest Logistic Regression - plot-multinomial-and-one-vs-rest-logistic-regression-49203) |
- 📖 Regularization Path of L1-Logistic Regression - regularization-path-of-l1-logistic-regression-49204) |
- 📖 Logistic Regression Model - logistic-regression-model-49205) |
- 📖 Comparison of Covariance Estimators - comparison-of-covariance-estimators-49206) |
- 📖 Robust Covariance Estimation and Mahalanobis Distances Relevance - robust-covariance-estimation-and-mahalanobis-distances-relevance-49207) |
- 📖 Sparse Inverse Covariance Estimation - sparse-inverse-covariance-estimation-49295) |
- 📖 Multiclass Sparse Logistic Regression - multiclass-sparse-logistic-regression-49296) |
- 📖 MNIST Multinomial Logistic Regression - mnist-multinomial-logistic-regression-49297) |
- 📖 Kernel Density Estimate of Species Distributions - kernel-density-estimate-of-species-distributions-49299) |
- 📖 Comparison Between Grid Search and Successive Halving - comparison-between-grid-search-and-successive-halving-49304) |
- 📖 Successive Halving Iterations - successive-halving-iterations-49305) |
- 📖 Feature Selection for SVC on Iris Dataset - feature-selection-for-svc-on-iris-dataset-49306) |
- 📖 Support Vector Regression - support-vector-regression-49310) |
- 📖 Scaling Regularization Parameter for SVMs - scaling-regularization-parameter-for-svms-49311) |
- 📖 SVM Tie Breaking - svm-tie-breaking-49312) |
- 📖 Swiss Roll and Swiss-Hole Reduction - swiss-roll-and-swiss-hole-reduction-49313) |
- 📖 Categorical Data Transformation using TargetEncoder - categorical-data-transformation-using-targetencoder-49315) |
- 📖 Theil-Sen Regression with Python Scikit-Learn - theil-sen-regression-with-python-scikit-learn-49317) |
- 📖 Compressive Sensing Image Reconstruction - compressive-sensing-image-reconstruction-49318) |
- 📖 Plot Topics Extraction with NMF Lda - plot-topics-extraction-with-nmf-lda-49319) |
- 📖 Scikit-Learn Elastic-Net Regression Model - scikit-learn-elastic-net-regression-model-49320) |
- 📖 Transforming Target for Linear Regression - transforming-target-for-linear-regression-49321) |
- 📖 Multi-Output Decision Tree Regression - multi-output-decision-tree-regression-49322) |
- 📖 Decision Tree Regression - decision-tree-regression-49323) |
- 📖 Underfitting and Overfitting - underfitting-and-overfitting-49324) |
- 📖 Decision Tree Analysis - decision-tree-analysis-49325) |
- 📖 Plotting Validation Curves - plotting-validation-curves-49326) |
- 📖 Revealing Iris Dataset Structure via Factor Analysis - revealing-iris-dataset-structure-via-factor-analysis-49327) |
- 📖 Iris Flower Classification using Voting Classifier - iris-flower-classification-using-voting-classifier-49328) |
- 📖 Class Probabilities with VotingClassifier - class-probabilities-with-votingclassifier-49329) |
- 📖 Diabetes Prediction Using Voting Regressor - diabetes-prediction-using-voting-regressor-49330) |
- 📖 Hierarchical Clustering with Connectivity Constraints - hierarchical-clustering-with-connectivity-constraints-49331) |
- 📖 Scikit-Learn Libsvm GUI - scikit-learn-libsvm-gui-49333) |
- 📖 Wikipedia PageRank with Randomized SVD - wikipedia-pagerank-with-randomized-svd-49334) |
- 📖 Evaluating Machine Learning Model Quality - evaluating-machine-learning-model-quality-71124) |

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