scikit-learn Practice Plus with AI and Hands-On Labs | scikit-learn Skill Tree

Course in scikit-learn Skill Tree

scikit-learn Practice Plus

Beginner

Machine LearningPandasPythonscikit-learn

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Intro
Syllabus

261 Labs

Mastering Decision TreesStart

Understanding Validation CurvesStart

Clustering and InsightsStart

Mastering naive bayesStart

Mastering Linear RegressionStart

Predicting Flower Types with Nearest NeighborsStart

Understanding Metrics and ScoringStart

Manifold Learning on Spherical DataStart

Faces Dataset DecompositionsStart

Random Classification Dataset PlottingStart

Scikit-Learn Make_multilabel_classificationStart

Swiss Roll and Swiss-Hole ReductionStart

Scikit-Learn Libsvm GUIStart

Vector Quantization With KBinsDiscretizerStart

Hierarchical Clustering With Scikit-LearnStart

Transforming the Prediction TargetStart

Feature AgglomerationStart

Species Distribution ModelingStart

Feature ExtractionStart

Comparison of F-Test and Mutual InformationStart

Data Scaling and TransformationStart

Curve Fitting With Bayesian Ridge RegressionStart

Demonstrating KBinsDiscretizer StrategiesStart

Lasso and Elastic NetStart

Logistic Regression ModelStart

Joint Feature Selection With Multi-Task LassoStart

SGD PenaltiesStart

Theil-Sen RegressionStart

Compressive Sensing Image ReconstructionStart

FeatureHasher and DictVectorizer ComparisonStart

Decision Tree RegressionStart

Multi-Output Decision Tree RegressionStart

Plot Elastic Net Precomputed Gram Matrix With Weighted SamplesStart

Plot Huber vs RidgeStart

Simple 1D Kernel Density EstimationStart

Scikit-Learn Lasso RegressionStart

Local Outlier Factor for Novelty DetectionStart

Outlier Detection With LOFStart

Sparse Signal Recovery With Orthogonal Matching PursuitStart

Plot SGD Separating HyperplaneStart

Density EstimationStart

K-Means ClusteringStart

Agglomerative Clustering on Digits DatasetStart

Step-by-Step Logistic RegressionStart

OPTICS Clustering AlgorithmStart

Biclustering in Scikit-LearnStart

Empirical Evaluation of K-Means InitializationStart

Regularization Path of L1- Logistic RegressionStart

Neighborhood Components AnalysisStart

Kernel Density Estimate of Species DistributionsStart

Support Vector RegressionStart

Affinity Propagation ClusteringStart

Hierarchical Clustering DendrogramStart

Comparing BIRCH and MiniBatchKMeansStart

Bisecting K-Means and Regular K-Means Performance ComparisonStart

Comparing Clustering AlgorithmsStart

Demo of HDBSCAN Clustering AlgorithmStart

Mean-Shift Clustering AlgorithmStart

Neural Network ModelsStart

ClusteringStart

Random Forest OOB Error EstimationStart

Pixel Importances With Parallel Forest of TreesStart

Gaussian Process Classification on Iris DatasetStart

Gaussian Process ClassificationStart

Gaussian Process Classification on XOR DatasetStart

Gaussian Process RegressionStart

Gaussian Process Regression: KernelsStart

Image Segmentation With Hierarchical ClusteringStart

Color Quantization Using K-MeansStart

Plot Dict Face PatchesStart

Gaussian Processes on Discrete Data StructuresStart

Spectral Clustering for Image SegmentationStart

Model-Based and Sequential Feature SelectionStart

SVM Tie BreakingStart

Cross-Validation on Digits DatasetStart

Early Stopping of Gradient BoostingStart

Cross ValidationStart

Plot GPR Co2Start

Linear Regression ExampleStart

Pairwise MetricsStart

Compare Cross Decomposition MethodsStart

Discretizing Continuous Features With KBinsDiscretizerStart

Boosted Decision Tree RegressionStart

Bias-Variance Decomposition With BaggingStart

Scikit-Learn Elastic-Net Regression ModelStart

Plot Agglomerative ClusteringStart

Map Data to a Normal DistributionStart

Nearest Neighbors ClassificationStart

SVM Classification Using Custom KernelStart

SVM Classifier on Iris DatasetStart

Recursive Feature EliminationStart

Diabetes Prediction Using Voting RegressorStart

Plot Forest IrisStart

Cross-Validation With Linear ModelsStart

Text Classification Using Out-of-Core LearningStart

Hierarchical Clustering With Connectivity ConstraintsStart

Imputation of Missing ValuesStart

SVM: Maximum Margin Separating HyperplaneStart

SVM for Unbalanced ClassesStart

Kernel ApproximationStart

Blind Source SeparationStart

FastICAStart

Iris DatasetStart

Principal Components AnalysisStart

Hyperparameter Optimization: Randomized Search vs Grid SearchStart

Sparse Coding With Precomputed DictionaryStart

Wikipedia PageRank With Randomized SVDStart

Decomposing Signals in ComponentsStart

Validation Curves: Plotting Scores to Evaluate ModelsStart

Post Pruning Decision TreesStart

Comparison of Covariance EstimatorsStart

Robust Covariance Estimation and Mahalanobis Distances RelevanceStart

Ridge RegressionStart

Robust Covariance Estimation in PythonStart

Comparing Online Solvers for Handwritten Digit ClassificationStart

Decision Tree AnalysisStart

Class Probabilities With VotingClassifierStart

Covariance EstimationStart

Preprocessing DataStart

Agglomerative Clustering MetricsStart

Logistic Regression Classifier on Iris DatasetStart

Scikit-Learn Multi-Class SGD ClassifierStart

Manifold LearningStart

Comparing Linear Bayesian RegressorsStart

Scikit-Learn IPCAStart

Lasso Model SelectionStart

Model Selection for Lasso RegressionStart

Linear and Quadratic Discriminant AnalysisStart

Plot Concentration PriorStart

Sparse Inverse Covariance EstimationStart

Gaussian Mixture ModelsStart

Plot Forest Hist Grad Boosting ComparisonStart

Clustering Analysis With Silhouette MethodStart

Plot Multinomial and One-vs-Rest Logistic RegressionStart

Comparing K-Means and MiniBatchKMeansStart

Nearest Centroid ClassificationStart

Spectral Biclustering AlgorithmStart

Spectral Co-Clustering AlgorithmStart

Permutation Feature ImportanceStart

Probabilistic Predictions With Gaussian Process ClassificationStart

Decision Trees on Iris DatasetStart

Nested Cross-Validation for Model SelectionStart

Permutation Test Score for ClassificationStart

Recursive Feature Elimination With Cross-ValidationStart

MNIST Multinomial Logistic RegressionStart

Scaling Regularization Parameter for SVMsStart

Plotting Validation CurvesStart

IsoTonic RegressionStart

Tuning Hyperparameters of an EstimatorStart

Digits ClassificationStart

Monotonic ConstraintsStart

Factor AnalysisStart

Feature SelectionStart

Scikit-Learn Confusion MatrixStart

Recognizing Hand-Written DigitsStart

Gradient Boosting RegularizationStart

Plot Topics Extraction With NMF LdaStart

DBSCAN Clustering AlgorithmStart

GMM InitializationStart

Active Learning Withel PropagationStart

Document Biclustering Using Spectral Co-Clustering AlgorithmStart

Partial Dependence and Individual Conditional ExpectationStart

ROC With Cross ValidationStart

Label Propagation LearningStart

Ensemble MethodsStart

Multi-Class AdaBoosted Decision TreesStart

Isotonic RegressionStart

Sparse Signal Regression With L1-Based ModelsStart

Plotting Learning CurvesStart

Non-Negative Least Squares RegressionStart

Quantile RegressionStart

Semi-Supervised LearningStart

Outlier DetectionStart

Sklearn TargetEncoderStart

Underfitting and OverfittingStart

Two-Class AdaBoostStart

Shrinkage Covariance EstimationStart

Plotting Predictions With Cross-ValidationStart

K-Means ClusteringStart

Multi-Dimensional ScalingStart

Robust Linear Estimator FittingStart

Model EvaluationStart

Caching Nearest NeighborsStart

Scikit-Learn GridSearchCVStart

Johnson-Lindenstrauss LemmaStart

Kernel PCAStart

Outlier Detection With Scikit-LearnStart

Digit Dataset AnalysisStart

Gaussian Mixture Model CovariancesStart

Gaussian Mixture Model SelectionStart

Plot Grid Search DigitsStart

Multiclass ROC Evaluation With Scikit-LearnStart

Semi-Supervised Classifiers on the Iris DatasetStart

Gradient Boosting Out-of-Bag EstimatesStart

Text Feature Extraction and EvaluationStart

Image Denoising With Kernel PCAStart

Anomaly Detection With Isolation ForestStart

Hashing Feature TransformationStart

Explicit Feature Map Approximation for RBF KernelsStart

Plotting Classification ProbabilityStart

Probability Calibration for 3-Class ClassificationStart

Plot Compare GPR KRRStart

Feature Transformations With Ensembles of TreesStart

Feature Importance With Random ForestStart

Multi-Layer Perceptron RegularizationStart

Discrete Versus Real AdaBoostStart

Scikit-Learn MLPClassifier: Stochastic Learning StrategiesStart

Kernel Density EstimationStart

Plot Pca vs LdaStart

Univariate Feature SelectionStart

Early Stopping of Stochastic Gradient DescentStart

SVM-AnovaStart

Approximate Nearest Neighbors in TSNEStart

K-Means Clustering on Handwritten DigitsStart

Linear Discriminant Analysis for ClassificationStart

Plot Sgdocsvm vs OcsvmStart

Plot Kernel Ridge RegressionStart

Polynomial Kernel Approximation With Scikit-LearnStart

Scikit-Learn Visualization APIStart

Plot Random Forest Regression MultioutputStart

Multiclass Sparse Logistic RegressionStart

Creating Visualizations With Display ObjectsStart

Scikit-Learn VotingClassifierStart

Comparison Between Grid Search and Successive HalvingStart

Plot Nca ClassificationStart

Scikit-Learn TransformedTargetRegressorStart

Successive Halving IterationsStart

Plot Digits PipeStart

Scikit-Learn Estimators and PipelinesStart

Scikit-Learn MLPClassifierStart

Gradient Boosting With Categorical FeaturesStart

Plot Pca vs Fa Model SelectionStart

Scikit-Learn PipelineStart

Balance Model Complexity and Cross-Validated ScoreStart

Text Document ClassificationStart

Digit Classification With RBM FeaturesStart

Comparison of Calibration of ClassifiersStart

Face Recognition With Eigenfaces and SVMsStart

Concatenating Multiple Feature Extraction MethodsStart

Detection Error Tradeoff CurveStart

Dimensionality Reduction With Pipeline and GridSearchCVStart

Effect of Varying Threshold for Self-TrainingStart

Probability Calibration CurvesStart

Class Likelihood Ratios to Measure Classification PerformanceStart

Plot PCR vs PLSStart

Multi-Label Document ClassificationStart

Column Transformer With Mixed TypesStart

Using `Set_output` APIStart

Anomaly Detection Algorithms ComparisonStart

Multiclass and Multioutput AlgorithmsStart

Precision-RecallStart

Semi-Supervised Text ClassificationStart

Impute Missing DataStart

Feature DiscretizationStart

Pipelines and Composite EstimatorsStart

Feature Scaling in Machine LearningStart

Scikit-Learn PipelineStart

Scikit-Learn Iterative ImputerStart

Manifold Learning on Handwritten DigitsStart

Scikit-Learn Classifier ComparisonStart

Teacher

Labby

Labby is the LabEx teacher.

Discussions

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