Course in Machine Learning Skill Tree

Machine Learning Practice Labs

Beginner

This course contains lots of labs for Machine Learning, each lab is a small Machine Learning project with detailed guidance and solutions. You can practice your Machine Learning skills by completing these labs, improve your coding skills, and learn how to write clean and efficient code.

Machine Learning

Previous Course Next Course

Intro
Syllabus

Introduction

This HTML Practice Labs course is designed to help you enhance your HTML skills through a series of hands-on exercises and projects. Whether you're a beginner or an experienced web developer, this course offers a practical approach to mastering the fundamentals of HTML.

🎯 Tasks

In this Course, you will learn:

How to structure web pages using HTML elements
How to create semantic and accessible HTML markup
How to format text, add images, and build navigation menus
How to incorporate links, lists, and tables into your web pages
How to apply best practices for writing clean and efficient HTML code

🏆 Achievements

After completing this Course, you will be able to:

Confidently create and structure HTML-based web pages
Demonstrate a strong understanding of HTML syntax and semantics
Implement various HTML features and elements to enhance the structure and appearance of web pages
Develop the ability to write clean, well-organized, and maintainable HTML code
Gain practical experience in HTML through the completion of hands-on practice labs

303 Labs

Adjusting for Chance in Clustering Performance EvaluationStart

Probability Calibration of ClassifiersStart

Plot Causal InterpretationStart

Classifier Chain EnsembleStart

Segmenting Greek Coins With Spectral ClusteringStart

Scikit-Learn Column TransformerStart

Manifold Learning ComparisonStart

Cross-Validation Techniques with Scikit-LearnStart

Comparing Dimensionality Reduction StrategiesStart

Gaussian Mixture ModelStart

Density Estimation With Gaussian Mixture ModelsStart

Gaussian Mixture Model Sine CurveStart

Prediction Intervals for Gradient Boosting RegressionStart

Gradient Boosting RegressionStart

Image Denoising Using Dictionary LearningStart

Inductive Clustering with Scikit-LearnStart

Iris Flower Binary Classification Using SVMStart

K-Means++ Clustering with Scikit-LearnStart

Semi-Supervised Learning Withel SpreadingStart

Scikit-Learn Lasso PathStart

LinearSVC Support VectorsStart

Understanding Model ComplexityStart

Face Completion With Multi-Output EstimatorsStart

Dimensionality Reduction With Neighborhood Components AnalysisStart

Linear Regression With Sparsity ExampleStart

Ordinary Least Squares and Ridge Regression VarianceStart

One-Class SVM for Novelty DetectionStart

Advanced Plotting With Partial DependenceStart

Principal Component Analysis on Iris DatasetStart

Plot Permutation ImportanceStart

Permutation Importance on Breast Cancer DatasetStart

Polynomial and Spline InterpolationStart

Prediction Latency With Scikit-Learn EstimatorsStart

Robust Linear Model EstimationStart

RBF SVM Parameter TuningStart

Nearest Neighbors RegressionStart

Scikit-Learn Ridge Regression ExampleStart

Convex Loss Functions ComparisonStart

Weighted Dataset Decision Function PlottingStart

Combine Predictors Using StackingStart

Visualizing Stock Market StructureStart

SVM Kernel Data ClassificationStart

Exploring Linear SVM ParametersStart

Non-Linear SVM ClassificationStart

Visualize High-Dimensional Data with t-SNEStart

Comparing Different Categorical EncodersStart

Support Vector Machine Weighted SamplesStart

Novelty and Outlier Detection Using Scikit-LearnStart

Random Projection Dimensionality ReductionStart

Curve Fitting With Bayesian Ridge RegressionStart

Nearest Neighbors ClassificationStart

Exploring K-Means Clustering with PythonStart

Compare Cross Decomposition MethodsStart

Plot Concentration PriorStart

SVM Classification Using Custom KernelStart

Cross-Validation on Digits DatasetStart

Feature Agglomeration for High-Dimensional DataStart

Agglomerative Clustering on Digits DatasetStart

Comparison of F-Test and Mutual InformationStart

Vector Quantization With KBinsDiscretizerStart

Faces Dataset DecompositionsStart

Gaussian Process Classification on Iris DatasetStart

Gaussian Process ClassificationStart

Gaussian Process Classification on XOR DatasetStart

Nonlinear Predictive Modeling Using Gaussian ProcessStart

Fit Gaussian Process Regression ModelStart

Gaussian Process Regression: KernelsStart

Early Stopping of Gradient BoostingStart

Blind Source SeparationStart

Independent Component Analysis with FastICA and PCAStart

Iris Flower Classification with Scikit-learnStart

SVM Classifier on Iris DatasetStart

Simple 1D Kernel Density EstimationStart

Active Learning Withel PropagationStart

Lasso and Elastic NetStart

Discriminant Analysis Classification AlgorithmsStart

Hierarchical Clustering With Scikit-LearnStart

Local Outlier Factor for Novelty DetectionStart

Outlier Detection With LOFStart

Logistic Regression ModelStart

Regularization Path of L1-Logistic RegressionStart

Comparison of Covariance EstimatorsStart

Robust Covariance Estimation and Mahalanobis Distances RelevanceStart

Manifold Learning on Spherical DataStart

Joint Feature Selection With Multi-Task LassoStart

Linear Regression ExampleStart

OPTICS Clustering AlgorithmStart

Principal Components AnalysisStart

Random Classification Dataset PlottingStart

Multilabel Dataset Generation with Scikit-LearnStart

Robust Covariance Estimation in PythonStart

Applying Regularization Techniques with SGDStart

Sparse Coding With Precomputed DictionaryStart

Support Vector RegressionStart

Swiss Roll and Swiss-Hole ReductionStart

Theil-Sen Regression with Python Scikit-LearnStart

Compressive Sensing Image ReconstructionStart

Decision Tree RegressionStart

Multi-Output Decision Tree RegressionStart

Scikit-Learn Libsvm GUIStart

Wikipedia PageRank With Randomized SVDStart

Nonlinear Regression with IsotonicStart

Neural Network ModelsStart

Gaussian Mixture ModelsStart

Manifold Learning with Scikit-LearnStart

Biclustering in Scikit-LearnStart

Decomposing Signals in ComponentsStart

Covariance Matrix Estimation with Scikit-LearnStart

Density Estimation Using Kernel DensityStart

Machine Learning Cross-Validation with PythonStart

Feature Extraction with Scikit-LearnStart

Imputation of Missing ValuesStart

Kernel Approximation Techniques in Scikit-LearnStart

Pairwise Metrics and Kernels in Scikit-LearnStart

Transforming the Prediction TargetStart

Boosted Decision Tree RegressionStart

Affinity Propagation ClusteringStart

Plot Agglomerative ClusteringStart

Agglomerative Clustering MetricsStart

Hierarchical Clustering DendrogramStart

Data Scaling and TransformationStart

Bias-Variance Decomposition With BaggingStart

Comparing BIRCH and MiniBatchKMeansStart

Bisecting K-Means and Regular K-Means Performance ComparisonStart

Comparing Clustering AlgorithmsStart

Image Segmentation With Hierarchical ClusteringStart

Scikit-Learn Confusion MatrixStart

Shrinkage Covariance EstimationStart

Cross-Validation With Linear ModelsStart

Plot Dict Face PatchesStart

Recognizing Hand-Written DigitsStart

Demonstrating KBinsDiscretizer StrategiesStart

Precompute Gram Matrix for ElasticNetStart

Random Forest OOB Error EstimationStart

Pixel Importances With Parallel Forest of TreesStart

Gaussian Mixture Model CovariancesStart

Gaussian Mixture Model SelectionStart

Probabilistic Predictions With Gaussian Process ClassificationStart

Plot GPR Co2Start

Gaussian Processes on Discrete Data StructuresStart

Gradient Boosting RegularizationStart

FeatureHasher and DictVectorizer ComparisonStart

Demo of HDBSCAN Clustering AlgorithmStart

Plot Huber vs RidgeStart

Incremental Principal Component Analysis on Iris DatasetStart

Logistic Regression Classifier on Iris DatasetStart

Explicit Feature Map Approximation for RBF KernelsStart

Empirical Evaluation of K-Means InitializationStart

Label Propagation LearningStart

Scikit-Learn Lasso RegressionStart

Step-by-Step Logistic RegressionStart

Map Data to a Normal DistributionStart

Visualize High-Dimensional Data with MDSStart

Mean-Shift Clustering AlgorithmStart

Gradient Boosting Monotonic ConstraintsStart

Neighborhood Components AnalysisStart

Nearest Centroid ClassificationStart

Sparse Signal Recovery With Orthogonal Matching PursuitStart

Plot Pca vs LdaStart

Spectral Clustering for Image SegmentationStart

Semi-Supervised Classifiers on the Iris DatasetStart

SVM: Maximum Margin Separating HyperplaneStart

SVM for Unbalanced ClassesStart

Scikit-Learn Multi-Class SGD ClassifierStart

Plot SGD Separating HyperplaneStart

Sparse Inverse Covariance EstimationStart

Species Distribution ModelingStart

Kernel Density Estimate of Species DistributionsStart

SVM Tie BreakingStart

Scikit-Learn Elastic-Net Regression ModelStart

Semi-Supervised Learning AlgorithmsStart

Unsupervised Clustering with K-MeansStart

Preprocessing Techniques in Scikit-LearnStart

Color Quantization Using K-MeansStart

Plot Compare GPR KRRStart

Post Pruning Decision TreesStart

Digits Classification using Scikit-LearnStart

Digit Dataset AnalysisStart

Discretizing Continuous Features With KBinsDiscretizerStart

Plot Forest Hist Grad Boosting ComparisonStart

Plot Forest IrisStart

Gaussian Mixture Model Initialization MethodsStart

Plot Grid Search DigitsStart

Decision Trees on Iris DatasetStart

Anomaly Detection With Isolation ForestStart

Nonparametric Isotonic Regression with Scikit-LearnStart

Exploring Johnson-Lindenstrauss Lemma with Random ProjectionsStart

Principal Component Analysis with Kernel PCAStart

Plot Kernel Ridge RegressionStart

Exploring K-Means Clustering AssumptionsStart

Clustering Analysis With Silhouette MethodStart

Sparse Signal Regression With L1-Based ModelsStart

Linear Discriminant Analysis for ClassificationStart

Plot Multinomial and One-vs-Rest Logistic RegressionStart

Comparing K-Means and MiniBatchKMeansStart

Scikit-Learn MLPClassifier: Stochastic Learning StrategiesStart

Nested Cross-Validation for Model SelectionStart

Non-Negative Least Squares RegressionStart

Detecting Outliers in Wine DataStart

Plot Pca vs Fa Model SelectionStart

Permutation Test Score for ClassificationStart

Quantile Regression with Scikit-LearnStart

Plot Random Forest Regression MultioutputStart

Hyperparameter Optimization: Randomized Search vs Grid SearchStart

Recursive Feature EliminationStart

Ridge Regression for Linear ModelingStart

ROC With Cross ValidationStart

Model-Based and Sequential Feature SelectionStart

Comparing Online Solvers for Handwritten Digit ClassificationStart

Spectral Biclustering AlgorithmStart

Spectral Co-Clustering AlgorithmStart

Comparison Between Grid Search and Successive HalvingStart

Scaling Regularization Parameter for SVMsStart

Plot Topics Extraction With NMF LdaStart

Decision Tree AnalysisStart

Plotting Validation CurvesStart

Revealing Iris Dataset Structure via Factor AnalysisStart

Class Probabilities With VotingClassifierStart

Diabetes Prediction Using Voting RegressorStart

Hierarchical Clustering With Connectivity ConstraintsStart

Tuning Hyperparameters of an EstimatorStart

Validation Curves: Plotting Scores to Evaluate ModelsStart

Partial Dependence and Individual Conditional ExpectationStart

Permutation Feature ImportanceStart

Discrete Versus Real AdaBoostStart

Multi-Class AdaBoosted Decision TreesStart

AdaBoost Decision Stump ClassificationStart

Comparing Linear Bayesian RegressorsStart

Document Biclustering Using Spectral Co-Clustering AlgorithmStart

Caching Nearest NeighborsStart

Probability Calibration for 3-Class ClassificationStart

Plotting Classification ProbabilityStart

Plotting Predictions With Cross-ValidationStart

DBSCAN Clustering AlgorithmStart

Image Denoising With Kernel PCAStart

Kernel Density EstimationStart

Feature Importance With Random ForestStart

Gradient Boosting Out-of-Bag EstimatesStart

Lasso Model SelectionStart

Model Selection for Lasso RegressionStart

Plotting Learning CurvesStart

Classify Handwritten Digits with MLP ClassifierStart

Optimizing Model Hyperparameters with GridSearchCVStart

Text Classification Using Out-of-Core LearningStart

Hashing Feature TransformationStart

Recursive Feature Elimination With Cross-ValidationStart

Robust Linear Estimator FittingStart

Early Stopping of Stochastic Gradient DescentStart

Plot Sgdocsvm vs OcsvmStart

Multiclass Sparse Logistic RegressionStart

Successive Halving IterationsStart

Categorical Data Transformation using TargetEncoderStart

Underfitting and OverfittingStart

Ensemble Methods Exploration with Scikit-LearnStart

Feature Selection with Scikit-LearnStart

Evaluating Machine Learning Model QualityStart

Plot Digits PipeStart

Scikit-Learn Estimators and PipelinesStart

Feature Transformations With Ensembles of TreesStart

Balance Model Complexity and Cross-Validated ScoreStart

Text Feature Extraction and EvaluationStart

K-Means Clustering on Handwritten DigitsStart

Multi-Layer Perceptron RegularizationStart

Multi-Label Document ClassificationStart

Plot Nca ClassificationStart

Outlier Detection Using Scikit-Learn AlgorithmsStart

Multiclass ROC Evaluation With Scikit-LearnStart

Scikit-Learn Visualization APIStart

Polynomial Kernel Approximation With Scikit-LearnStart

Effect of Varying Threshold for Self-TrainingStart

MNIST Multinomial Logistic RegressionStart

Iris Flower Classification using Voting ClassifierStart

Approximate Nearest Neighbors in TSNEStart

Creating Visualizations With Display ObjectsStart

Face Recognition With Eigenfaces and SVMsStart

Univariate Feature SelectionStart

Building Machine Learning Pipelines with Scikit-LearnStart

Concatenating Multiple Feature Extraction MethodsStart

Gradient Boosting With Categorical FeaturesStart

Class Likelihood Ratios to Measure Classification PerformanceStart

Impute Missing DataStart

Plot PCR vs PLSStart

Feature Selection for SVC on Iris DatasetStart

Transforming Target for Linear RegressionStart

Multiclass and Multioutput AlgorithmsStart

Anomaly Detection Algorithms ComparisonStart

Probability Calibration CurvesStart

Comparison of Calibration of ClassifiersStart

Dimensionality Reduction With Pipeline and GridSearchCVStart

Detection Error Tradeoff CurveStart

Precision-Recall Metric for Imbalanced ClassificationStart

Column Transformer With Mixed TypesStart

Digit Classification With RBM FeaturesStart

Semi-Supervised Text ClassificationStart

Using Set_output APIStart

Feature Discretization for ClassificationStart

Text Document ClassificationStart

Scikit-Learn Iterative ImputerStart

Manifold Learning on Handwritten DigitsStart

Constructing Scikit-Learn PipelinesStart

Feature Scaling in Machine LearningStart

Pipelines and Composite EstimatorsStart

Scikit-Learn Classifier ComparisonStart

Teacher

labby

Labby

Labby is the LabEx teacher.

Recommended For You

Machine Learning Practice Challenges

Machine Learning Practice Challenges

Machine Learning

Deploying MobileNet With TensorFlow.js and Flask

Deploying MobileNet With TensorFlow.js and Flask

CSSHTMLPythonLinuxFlaskJavaScriptMachine Learning

Deploying a Simple TensorFlow Model

Deploying a Simple TensorFlow Model

LinuxDockerMachine Learning