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[Machine Learning Practice Labs] This repository collects 303 of programming scenarios (labs and challenges) for Machine Learning Practice Labs. This course contains lots of labs for Machine Learning, each lab is a small Machine Learning project with detailed guidance and solutions. You can pract...
https://github.com/labex-labs/ml-practice-labs
List: ml-practice-labs
awesome awesome-list challenges course education hands-on labex labs machine-learning programming
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[Machine Learning Practice Labs] This repository collects 303 of programming scenarios (labs and challenges) for Machine Learning Practice Labs. This course contains lots of labs for Machine Learning, each lab is a small Machine Learning project with detailed guidance and solutions. You can pract...
- Host: GitHub
- URL: https://github.com/labex-labs/ml-practice-labs
- Owner: labex-labs
- Created: 2024-05-24T09:06:47.000Z (6 months ago)
- Default Branch: master
- Last Pushed: 2024-08-01T01:31:14.000Z (4 months ago)
- Last Synced: 2024-08-02T04:25:10.576Z (4 months ago)
- Topics: awesome, awesome-list, challenges, course, education, hands-on, labex, labs, machine-learning, programming
- Homepage: https://labex.io/skilltrees/ml
- Size: 42 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Machine Learning Practice Labs
[](https://labex.io/courses/ml-practice-labs)
[](https://labex.io/courses/ml-practice-labs)
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.
## Environment
LabEx is an interactive, hands-on learning platform dedicated to coding and technology. It combines labs, AI assistance, and virtual machines to provide a no-video, practical learning experience.
- A strict “Learn by Doing” approach with exclusive hands-on labs and no videos.
- Interactive online environments within the browser, with automated step-by-step checks.
- A structured content organization with the Skill Tree based learning system.
- A growing learning resource of 30 Skill Trees and over 6,000 Labs.
- The AI learning assistant Labby, built on ChatGPT, providing a conversational learning experience.
Learn more about [LabEx VM](https://support.labex.io/using-labex/virtual-machine).
## Exercises
| Index | Name | Difficulty | Practice |
|---------|---------------------------------------------------------|--------------|----------------------------------------------------------------------------------------------------------------------------------------------|
| 001 | 📖 Adjusting for Chance in Clustering Performance Eva... | ★☆☆ | Start Lab |
| 002 | 📖 Probability Calibration of Classifiers | ★☆☆ | Start Lab |
| 003 | 📖 Plot Causal Interpretation | ★☆☆ | Start Lab |
| 004 | 📖 Classifier Chain Ensemble | ★☆☆ | Start Lab |
| 005 | 📖 Segmenting Greek Coins with Spectral Clustering | ★☆☆ | Start Lab |
| 006 | 📖 Scikit-Learn Column Transformer | ★☆☆ | Start Lab |
| 007 | 📖 Manifold Learning Comparison | ★☆☆ | Start Lab |
| 008 | 📖 Cross-Validation Techniques with Scikit-Learn | ★☆☆ | Start Lab |
| 009 | 📖 Comparing Dimensionality Reduction Strategies | ★☆☆ | Start Lab |
| 010 | 📖 Gaussian Mixture Model | ★☆☆ | Start Lab |
| 011 | 📖 Density Estimation with Gaussian Mixture Models | ★☆☆ | Start Lab |
| 012 | 📖 Gaussian Mixture Model Sine Curve | ★☆☆ | Start Lab |
| 013 | 📖 Prediction Intervals for Gradient Boosting Regress... | ★☆☆ | Start Lab |
| 014 | 📖 Gradient Boosting Regression | ★☆☆ | Start Lab |
| 015 | 📖 Image Denoising Using Dictionary Learning | ★☆☆ | Start Lab |
| 016 | 📖 Inductive Clustering with Scikit-Learn | ★☆☆ | Start Lab |
| 017 | 📖 Iris Flower Binary Classification Using SVM | ★☆☆ | Start Lab |
| 018 | 📖 K-Means++ Clustering with Scikit-Learn | ★☆☆ | Start Lab |
| 019 | 📖 Semi-Supervised Learning Withel Spreading | ★☆☆ | Start Lab |
| 020 | 📖 Scikit-Learn Lasso Path | ★☆☆ | Start Lab |
| 021 | 📖 LinearSVC Support Vectors | ★☆☆ | Start Lab |
| 022 | 📖 Understanding Model Complexity | ★☆☆ | Start Lab |
| 023 | 📖 Face Completion with Multi-Output Estimators | ★☆☆ | Start Lab |
| 024 | 📖 Dimensionality Reduction with Neighborhood Compone... | ★☆☆ | Start Lab |
| 025 | 📖 Linear Regression with Sparsity Example | ★☆☆ | Start Lab |
| 026 | 📖 Ordinary Least Squares and Ridge Regression Varian... | ★☆☆ | Start Lab |
| 027 | 📖 One-Class SVM for Novelty Detection | ★☆☆ | Start Lab |
| 028 | 📖 Advanced Plotting with Partial Dependence | ★☆☆ | Start Lab |
| 029 | 📖 Principal Component Analysis on Iris Dataset | ★☆☆ | Start Lab |
| 030 | 📖 Plot Permutation Importance | ★☆☆ | Start Lab |
| 031 | 📖 Permutation Importance on Breast Cancer Dataset | ★☆☆ | Start Lab |
| 032 | 📖 Polynomial and Spline Interpolation | ★☆☆ | Start Lab |
| 033 | 📖 Prediction Latency with Scikit-Learn Estimators | ★☆☆ | Start Lab |
| 034 | 📖 Robust Linear Model Estimation | ★☆☆ | Start Lab |
| 035 | 📖 RBF SVM Parameter Tuning | ★☆☆ | Start Lab |
| 036 | 📖 Nearest Neighbors Regression | ★☆☆ | Start Lab |
| 037 | 📖 Scikit-Learn Ridge Regression Example | ★☆☆ | Start Lab |
| 038 | 📖 Convex Loss Functions Comparison | ★☆☆ | Start Lab |
| 039 | 📖 Weighted Dataset Decision Function Plotting | ★☆☆ | Start Lab |
| 040 | 📖 Combine Predictors Using Stacking | ★☆☆ | Start Lab |
| 041 | 📖 Visualizing Stock Market Structure | ★☆☆ | Start Lab |
| 042 | 📖 SVM Kernel Data Classification | ★☆☆ | Start Lab |
| 043 | 📖 Exploring Linear SVM Parameters | ★☆☆ | Start Lab |
| 044 | 📖 Non-Linear SVM Classification | ★☆☆ | Start Lab |
| 045 | 📖 Visualize High-Dimensional Data with t-SNE | ★☆☆ | Start Lab |
| 046 | 📖 Comparing Different Categorical Encoders | ★☆☆ | Start Lab |
| 047 | 📖 Support Vector Machine Weighted Samples | ★☆☆ | Start Lab |
| 048 | 📖 Novelty and Outlier Detection Using Scikit-Learn | ★☆☆ | Start Lab |
| 049 | 📖 Random Projection Dimensionality Reduction | ★☆☆ | Start Lab |
| 050 | 📖 Curve Fitting with Bayesian Ridge Regression | ★☆☆ | Start Lab |
| 051 | 📖 Nearest Neighbors Classification | ★☆☆ | Start Lab |
| 052 | 📖 Exploring K-Means Clustering with Python | ★☆☆ | Start Lab |
| 053 | 📖 Compare Cross Decomposition Methods | ★☆☆ | Start Lab |
| 054 | 📖 Plot Concentration Prior | ★☆☆ | Start Lab |
| 055 | 📖 SVM Classification Using Custom Kernel | ★☆☆ | Start Lab |
| 056 | 📖 Cross-Validation on Digits Dataset | ★☆☆ | Start Lab |
| 057 | 📖 Feature Agglomeration for High-Dimensional Data | ★☆☆ | Start Lab |
| 058 | 📖 Agglomerative Clustering on Digits Dataset | ★☆☆ | Start Lab |
| 059 | 📖 Comparison of F-Test and Mutual Information | ★☆☆ | Start Lab |
| 060 | 📖 Vector Quantization with KBinsDiscretizer | ★☆☆ | Start Lab |
| 061 | 📖 Faces Dataset Decompositions | ★☆☆ | Start Lab |
| 062 | 📖 Gaussian Process Classification on Iris Dataset | ★☆☆ | Start Lab |
| 063 | 📖 Gaussian Process Classification | ★☆☆ | Start Lab |
| 064 | 📖 Gaussian Process Classification on XOR Dataset | ★☆☆ | Start Lab |
| 065 | 📖 Nonlinear Predictive Modeling Using Gaussian Proce... | ★☆☆ | Start Lab |
| 066 | 📖 Fit Gaussian Process Regression Model | ★☆☆ | Start Lab |
| 067 | 📖 Gaussian Process Regression: Kernels | ★☆☆ | Start Lab |
| 068 | 📖 Early Stopping of Gradient Boosting | ★☆☆ | Start Lab |
| 069 | 📖 Blind Source Separation | ★☆☆ | Start Lab |
| 070 | 📖 Independent Component Analysis with FastICA and PC... | ★☆☆ | Start Lab |
| 071 | 📖 Iris Flower Classification with Scikit-learn | ★☆☆ | Start Lab |
| 072 | 📖 SVM Classifier on Iris Dataset | ★☆☆ | Start Lab |
| 073 | 📖 Simple 1D Kernel Density Estimation | ★☆☆ | Start Lab |
| 074 | 📖 Active Learning Withel Propagation | ★☆☆ | Start Lab |
| 075 | 📖 Lasso and Elastic Net | ★☆☆ | Start Lab |
| 076 | 📖 Discriminant Analysis Classification Algorithms | ★☆☆ | Start Lab |
| 077 | 📖 Hierarchical Clustering with Scikit-Learn | ★☆☆ | Start Lab |
| 078 | 📖 Local Outlier Factor for Novelty Detection | ★☆☆ | Start Lab |
| 079 | 📖 Outlier Detection with LOF | ★☆☆ | Start Lab |
| 080 | 📖 Logistic Regression Model | ★☆☆ | Start Lab |
| 081 | 📖 Regularization Path of L1-Logistic Regression | ★☆☆ | Start Lab |
| 082 | 📖 Comparison of Covariance Estimators | ★☆☆ | Start Lab |
| 083 | 📖 Robust Covariance Estimation and Mahalanobis Dista... | ★☆☆ | Start Lab |
| 084 | 📖 Manifold Learning on Spherical Data | ★☆☆ | Start Lab |
| 085 | 📖 Joint Feature Selection with Multi-Task Lasso | ★☆☆ | Start Lab |
| 086 | 📖 Linear Regression Example | ★☆☆ | Start Lab |
| 087 | 📖 OPTICS Clustering Algorithm | ★☆☆ | Start Lab |
| 088 | 📖 Principal Components Analysis | ★☆☆ | Start Lab |
| 089 | 📖 Random Classification Dataset Plotting | ★☆☆ | Start Lab |
| 090 | 📖 Multilabel Dataset Generation with Scikit-Learn | ★☆☆ | Start Lab |
| 091 | 📖 Robust Covariance Estimation in Python | ★☆☆ | Start Lab |
| 092 | 📖 Applying Regularization Techniques with SGD | ★☆☆ | Start Lab |
| 093 | 📖 Sparse Coding with Precomputed Dictionary | ★☆☆ | Start Lab |
| 094 | 📖 Support Vector Regression | ★☆☆ | Start Lab |
| 095 | 📖 Swiss Roll and Swiss-Hole Reduction | ★☆☆ | Start Lab |
| 096 | 📖 Theil-Sen Regression with Python Scikit-Learn | ★☆☆ | Start Lab |
| 097 | 📖 Compressive Sensing Image Reconstruction | ★☆☆ | Start Lab |
| 098 | 📖 Decision Tree Regression | ★☆☆ | Start Lab |
| 099 | 📖 Multi-Output Decision Tree Regression | ★☆☆ | Start Lab |
| 100 | 📖 Scikit-Learn Libsvm GUI | ★☆☆ | Start Lab |
| 101 | 📖 Wikipedia PageRank with Randomized SVD | ★☆☆ | Start Lab |
| 102 | 📖 Nonlinear Regression with Isotonic | ★☆☆ | Start Lab |
| 103 | 📖 Neural Network Models | ★☆☆ | Start Lab |
| 104 | 📖 Gaussian Mixture Models | ★☆☆ | Start Lab |
| 105 | 📖 Manifold Learning with Scikit-Learn | ★☆☆ | Start Lab |
| 106 | 📖 Biclustering in Scikit-Learn | ★☆☆ | Start Lab |
| 107 | 📖 Decomposing Signals in Components | ★☆☆ | Start Lab |
| 108 | 📖 Covariance Matrix Estimation with Scikit-Learn | ★☆☆ | Start Lab |
| 109 | 📖 Density Estimation Using Kernel Density | ★☆☆ | Start Lab |
| 110 | 📖 Machine Learning Cross-Validation with Python | ★☆☆ | Start Lab |
| 111 | 📖 Feature Extraction with Scikit-Learn | ★☆☆ | Start Lab |
| 112 | 📖 Imputation of Missing Values | ★☆☆ | Start Lab |
| 113 | 📖 Kernel Approximation Techniques in Scikit-Learn | ★☆☆ | Start Lab |
| 114 | 📖 Pairwise Metrics and Kernels in Scikit-Learn | ★☆☆ | Start Lab |
| 115 | 📖 Transforming the Prediction Target | ★☆☆ | Start Lab |
| 116 | 📖 Boosted Decision Tree Regression | ★☆☆ | Start Lab |
| 117 | 📖 Affinity Propagation Clustering | ★☆☆ | Start Lab |
| 118 | 📖 Plot Agglomerative Clustering | ★☆☆ | Start Lab |
| 119 | 📖 Agglomerative Clustering Metrics | ★☆☆ | Start Lab |
| 120 | 📖 Hierarchical Clustering Dendrogram | ★☆☆ | Start Lab |
| 121 | 📖 Data Scaling and Transformation | ★☆☆ | Start Lab |
| 122 | 📖 Bias-Variance Decomposition with Bagging | ★☆☆ | Start Lab |
| 123 | 📖 Comparing BIRCH and MiniBatchKMeans | ★☆☆ | Start Lab |
| 124 | 📖 Bisecting K-Means and Regular K-Means Performance ... | ★☆☆ | Start Lab |
| 125 | 📖 Comparing Clustering Algorithms | ★☆☆ | Start Lab |
| 126 | 📖 Image Segmentation with Hierarchical Clustering | ★☆☆ | Start Lab |
| 127 | 📖 Scikit-Learn Confusion Matrix | ★☆☆ | Start Lab |
| 128 | 📖 Shrinkage Covariance Estimation | ★☆☆ | Start Lab |
| 129 | 📖 Cross-Validation with Linear Models | ★☆☆ | Start Lab |
| 130 | 📖 Plot Dict Face Patches | ★☆☆ | Start Lab |
| 131 | 📖 Recognizing Hand-Written Digits | ★☆☆ | Start Lab |
| 132 | 📖 Demonstrating KBinsDiscretizer Strategies | ★☆☆ | Start Lab |
| 133 | 📖 Precompute Gram Matrix for ElasticNet | ★☆☆ | Start Lab |
| 134 | 📖 Random Forest OOB Error Estimation | ★☆☆ | Start Lab |
| 135 | 📖 Pixel Importances with Parallel Forest of Trees | ★☆☆ | Start Lab |
| 136 | 📖 Gaussian Mixture Model Covariances | ★☆☆ | Start Lab |
| 137 | 📖 Gaussian Mixture Model Selection | ★☆☆ | Start Lab |
| 138 | 📖 Probabilistic Predictions with Gaussian Process Cl... | ★☆☆ | Start Lab |
| 139 | 📖 Plot GPR Co2 | ★☆☆ | Start Lab |
| 140 | 📖 Gaussian Processes on Discrete Data Structures | ★☆☆ | Start Lab |
| 141 | 📖 Gradient Boosting Regularization | ★☆☆ | Start Lab |
| 142 | 📖 FeatureHasher and DictVectorizer Comparison | ★☆☆ | Start Lab |
| 143 | 📖 Demo of HDBSCAN Clustering Algorithm | ★☆☆ | Start Lab |
| 144 | 📖 Plot Huber vs Ridge | ★☆☆ | Start Lab |
| 145 | 📖 Incremental Principal Component Analysis on Iris D... | ★☆☆ | Start Lab |
| 146 | 📖 Logistic Regression Classifier on Iris Dataset | ★☆☆ | Start Lab |
| 147 | 📖 Explicit Feature Map Approximation for RBF Kernels | ★☆☆ | Start Lab |
| 148 | 📖 Empirical Evaluation of K-Means Initialization | ★☆☆ | Start Lab |
| 149 | 📖 Label Propagation Learning | ★☆☆ | Start Lab |
| 150 | 📖 Scikit-Learn Lasso Regression | ★☆☆ | Start Lab |
| 151 | 📖 Step-by-Step Logistic Regression | ★☆☆ | Start Lab |
| 152 | 📖 Map Data to a Normal Distribution | ★☆☆ | Start Lab |
| 153 | 📖 Visualize High-Dimensional Data with MDS | ★☆☆ | Start Lab |
| 154 | 📖 Mean-Shift Clustering Algorithm | ★☆☆ | Start Lab |
| 155 | 📖 Gradient Boosting Monotonic Constraints | ★☆☆ | Start Lab |
| 156 | 📖 Neighborhood Components Analysis | ★☆☆ | Start Lab |
| 157 | 📖 Nearest Centroid Classification | ★☆☆ | Start Lab |
| 158 | 📖 Sparse Signal Recovery with Orthogonal Matching Pu... | ★☆☆ | Start Lab |
| 159 | 📖 Plot Pca vs Lda | ★☆☆ | Start Lab |
| 160 | 📖 Spectral Clustering for Image Segmentation | ★☆☆ | Start Lab |
| 161 | 📖 Semi-Supervised Classifiers on the Iris Dataset | ★☆☆ | Start Lab |
| 162 | 📖 SVM: Maximum Margin Separating Hyperplane | ★☆☆ | Start Lab |
| 163 | 📖 SVM for Unbalanced Classes | ★☆☆ | Start Lab |
| 164 | 📖 Scikit-Learn Multi-Class SGD Classifier | ★☆☆ | Start Lab |
| 165 | 📖 Plot SGD Separating Hyperplane | ★☆☆ | Start Lab |
| 166 | 📖 Sparse Inverse Covariance Estimation | ★☆☆ | Start Lab |
| 167 | 📖 Species Distribution Modeling | ★☆☆ | Start Lab |
| 168 | 📖 Kernel Density Estimate of Species Distributions | ★☆☆ | Start Lab |
| 169 | 📖 SVM Tie Breaking | ★☆☆ | Start Lab |
| 170 | 📖 Scikit-Learn Elastic-Net Regression Model | ★☆☆ | Start Lab |
| 171 | 📖 Semi-Supervised Learning Algorithms | ★☆☆ | Start Lab |
| 172 | 📖 Unsupervised Clustering with K-Means | ★☆☆ | Start Lab |
| 173 | 📖 Preprocessing Techniques in Scikit-Learn | ★☆☆ | Start Lab |
| 174 | 📖 Color Quantization Using K-Means | ★☆☆ | Start Lab |
| 175 | 📖 Plot Compare GPR KRR | ★☆☆ | Start Lab |
| 176 | 📖 Post Pruning Decision Trees | ★☆☆ | Start Lab |
| 177 | 📖 Digits Classification using Scikit-Learn | ★☆☆ | Start Lab |
| 178 | 📖 Digit Dataset Analysis | ★☆☆ | Start Lab |
| 179 | 📖 Discretizing Continuous Features with KBinsDiscret... | ★☆☆ | Start Lab |
| 180 | 📖 Plot Forest Hist Grad Boosting Comparison | ★☆☆ | Start Lab |
| 181 | 📖 Plot Forest Iris | ★☆☆ | Start Lab |
| 182 | 📖 Gaussian Mixture Model Initialization Methods | ★☆☆ | Start Lab |
| 183 | 📖 Plot Grid Search Digits | ★☆☆ | Start Lab |
| 184 | 📖 Decision Trees on Iris Dataset | ★☆☆ | Start Lab |
| 185 | 📖 Anomaly Detection with Isolation Forest | ★☆☆ | Start Lab |
| 186 | 📖 Nonparametric Isotonic Regression with Scikit-Lear... | ★☆☆ | Start Lab |
| 187 | 📖 Exploring Johnson-Lindenstrauss Lemma with Random ... | ★☆☆ | Start Lab |
| 188 | 📖 Principal Component Analysis with Kernel PCA | ★☆☆ | Start Lab |
| 189 | 📖 Plot Kernel Ridge Regression | ★☆☆ | Start Lab |
| 190 | 📖 Exploring K-Means Clustering Assumptions | ★☆☆ | Start Lab |
| 191 | 📖 Clustering Analysis with Silhouette Method | ★☆☆ | Start Lab |
| 192 | 📖 Sparse Signal Regression with L1-Based Models | ★☆☆ | Start Lab |
| 193 | 📖 Linear Discriminant Analysis for Classification | ★☆☆ | Start Lab |
| 194 | 📖 Plot Multinomial and One-vs-Rest Logistic Regressi... | ★☆☆ | Start Lab |
| 195 | 📖 Comparing K-Means and MiniBatchKMeans | ★☆☆ | Start Lab |
| 196 | 📖 Scikit-Learn MLPClassifier: Stochastic Learning St... | ★☆☆ | Start Lab |
| 197 | 📖 Nested Cross-Validation for Model Selection | ★☆☆ | Start Lab |
| 198 | 📖 Non-Negative Least Squares Regression | ★☆☆ | Start Lab |
| 199 | 📖 Detecting Outliers in Wine Data | ★☆☆ | Start Lab |
| 200 | 📖 Plot Pca vs Fa Model Selection | ★☆☆ | Start Lab |
| 201 | 📖 Permutation Test Score for Classification | ★☆☆ | Start Lab |
| 202 | 📖 Quantile Regression with Scikit-Learn | ★☆☆ | Start Lab |
| 203 | 📖 Plot Random Forest Regression Multioutput | ★☆☆ | Start Lab |
| 204 | 📖 Hyperparameter Optimization: Randomized Search vs ... | ★☆☆ | Start Lab |
| 205 | 📖 Recursive Feature Elimination | ★☆☆ | Start Lab |
| 206 | 📖 Ridge Regression for Linear Modeling | ★☆☆ | Start Lab |
| 207 | 📖 ROC with Cross Validation | ★☆☆ | Start Lab |
| 208 | 📖 Model-Based and Sequential Feature Selection | ★☆☆ | Start Lab |
| 209 | 📖 Comparing Online Solvers for Handwritten Digit Cla... | ★☆☆ | Start Lab |
| 210 | 📖 Spectral Biclustering Algorithm | ★☆☆ | Start Lab |
| 211 | 📖 Spectral Co-Clustering Algorithm | ★☆☆ | Start Lab |
| 212 | 📖 Comparison Between Grid Search and Successive Halv... | ★☆☆ | Start Lab |
| 213 | 📖 Scaling Regularization Parameter for SVMs | ★☆☆ | Start Lab |
| 214 | 📖 Plot Topics Extraction with NMF Lda | ★☆☆ | Start Lab |
| 215 | 📖 Decision Tree Analysis | ★☆☆ | Start Lab |
| 216 | 📖 Plotting Validation Curves | ★☆☆ | Start Lab |
| 217 | 📖 Revealing Iris Dataset Structure via Factor Analys... | ★☆☆ | Start Lab |
| 218 | 📖 Class Probabilities with VotingClassifier | ★☆☆ | Start Lab |
| 219 | 📖 Diabetes Prediction Using Voting Regressor | ★☆☆ | Start Lab |
| 220 | 📖 Hierarchical Clustering with Connectivity Constrai... | ★☆☆ | Start Lab |
| 221 | 📖 Tuning Hyperparameters of an Estimator | ★☆☆ | Start Lab |
| 222 | 📖 Validation Curves: Plotting Scores to Evaluate Mod... | ★☆☆ | Start Lab |
| 223 | 📖 Partial Dependence and Individual Conditional Expe... | ★☆☆ | Start Lab |
| 224 | 📖 Permutation Feature Importance | ★☆☆ | Start Lab |
| 225 | 📖 Discrete Versus Real AdaBoost | ★☆☆ | Start Lab |
| 226 | 📖 Multi-Class AdaBoosted Decision Trees | ★☆☆ | Start Lab |
| 227 | 📖 AdaBoost Decision Stump Classification | ★☆☆ | Start Lab |
| 228 | 📖 Comparing Linear Bayesian Regressors | ★☆☆ | Start Lab |
| 229 | 📖 Document Biclustering Using Spectral Co-Clustering... | ★☆☆ | Start Lab |
| 230 | 📖 Caching Nearest Neighbors | ★☆☆ | Start Lab |
| 231 | 📖 Probability Calibration for 3-Class Classification | ★☆☆ | Start Lab |
| 232 | 📖 Plotting Classification Probability | ★☆☆ | Start Lab |
| 233 | 📖 Plotting Predictions with Cross-Validation | ★☆☆ | Start Lab |
| 234 | 📖 DBSCAN Clustering Algorithm | ★☆☆ | Start Lab |
| 235 | 📖 Image Denoising with Kernel PCA | ★☆☆ | Start Lab |
| 236 | 📖 Kernel Density Estimation | ★☆☆ | Start Lab |
| 237 | 📖 Feature Importance with Random Forest | ★☆☆ | Start Lab |
| 238 | 📖 Gradient Boosting Out-of-Bag Estimates | ★☆☆ | Start Lab |
| 239 | 📖 Lasso Model Selection | ★☆☆ | Start Lab |
| 240 | 📖 Model Selection for Lasso Regression | ★☆☆ | Start Lab |
| 241 | 📖 Plotting Learning Curves | ★☆☆ | Start Lab |
| 242 | 📖 Classify Handwritten Digits with MLP Classifier | ★☆☆ | Start Lab |
| 243 | 📖 Optimizing Model Hyperparameters with GridSearchCV | ★☆☆ | Start Lab |
| 244 | 📖 Text Classification Using Out-of-Core Learning | ★☆☆ | Start Lab |
| 245 | 📖 Hashing Feature Transformation | ★☆☆ | Start Lab |
| 246 | 📖 Recursive Feature Elimination with Cross-Validatio... | ★☆☆ | Start Lab |
| 247 | 📖 Robust Linear Estimator Fitting | ★☆☆ | Start Lab |
| 248 | 📖 Early Stopping of Stochastic Gradient Descent | ★☆☆ | Start Lab |
| 249 | 📖 Plot Sgdocsvm vs Ocsvm | ★☆☆ | Start Lab |
| 250 | 📖 Multiclass Sparse Logistic Regression | ★☆☆ | Start Lab |
| 251 | 📖 Successive Halving Iterations | ★☆☆ | Start Lab |
| 252 | 📖 Categorical Data Transformation using TargetEncode... | ★☆☆ | Start Lab |
| 253 | 📖 Underfitting and Overfitting | ★☆☆ | Start Lab |
| 254 | 📖 Ensemble Methods Exploration with Scikit-Learn | ★☆☆ | Start Lab |
| 255 | 📖 Feature Selection with Scikit-Learn | ★☆☆ | Start Lab |
| 256 | 📖 Evaluating Machine Learning Model Quality | ★☆☆ | Start Lab |
| 257 | 📖 Plot Digits Pipe | ★☆☆ | Start Lab |
| 258 | 📖 Scikit-Learn Estimators and Pipelines | ★☆☆ | Start Lab |
| 259 | 📖 Feature Transformations with Ensembles of Trees | ★☆☆ | Start Lab |
| 260 | 📖 Balance Model Complexity and Cross-Validated Score | ★☆☆ | Start Lab |
| 261 | 📖 Text Feature Extraction and Evaluation | ★☆☆ | Start Lab |
| 262 | 📖 K-Means Clustering on Handwritten Digits | ★☆☆ | Start Lab |
| 263 | 📖 Multi-Layer Perceptron Regularization | ★☆☆ | Start Lab |
| 264 | 📖 Multi-Label Document Classification | ★☆☆ | Start Lab |
| 265 | 📖 Plot Nca Classification | ★☆☆ | Start Lab |
| 266 | 📖 Outlier Detection Using Scikit-Learn Algorithms | ★☆☆ | Start Lab |
| 267 | 📖 Multiclass ROC Evaluation with Scikit-Learn | ★☆☆ | Start Lab |
| 268 | 📖 Scikit-Learn Visualization API | ★☆☆ | Start Lab |
| 269 | 📖 Polynomial Kernel Approximation with Scikit-Learn | ★☆☆ | Start Lab |
| 270 | 📖 Effect of Varying Threshold for Self-Training | ★☆☆ | Start Lab |
| 271 | 📖 MNIST Multinomial Logistic Regression | ★☆☆ | Start Lab |
| 272 | 📖 Iris Flower Classification using Voting Classifier | ★☆☆ | Start Lab |
| 273 | 📖 Approximate Nearest Neighbors in TSNE | ★☆☆ | Start Lab |
| 274 | 📖 Creating Visualizations with Display Objects | ★☆☆ | Start Lab |
| 275 | 📖 Face Recognition with Eigenfaces and SVMs | ★☆☆ | Start Lab |
| 276 | 📖 Univariate Feature Selection | ★☆☆ | Start Lab |
| 277 | 📖 Building Machine Learning Pipelines with Scikit-Le... | ★☆☆ | Start Lab |
| 278 | 📖 Concatenating Multiple Feature Extraction Methods | ★☆☆ | Start Lab |
| 279 | 📖 Gradient Boosting with Categorical Features | ★☆☆ | Start Lab |
| 280 | 📖 Class Likelihood Ratios to Measure Classification ... | ★☆☆ | Start Lab |
| 281 | 📖 Impute Missing Data | ★☆☆ | Start Lab |
| 282 | 📖 Plot PCR vs PLS | ★☆☆ | Start Lab |
| 283 | 📖 Feature Selection for SVC on Iris Dataset | ★☆☆ | Start Lab |
| 284 | 📖 Transforming Target for Linear Regression | ★☆☆ | Start Lab |
| 285 | 📖 Multiclass and Multioutput Algorithms | ★☆☆ | Start Lab |
| 286 | 📖 Anomaly Detection Algorithms Comparison | ★☆☆ | Start Lab |
| 287 | 📖 Probability Calibration Curves | ★☆☆ | Start Lab |
| 288 | 📖 Comparison of Calibration of Classifiers | ★☆☆ | Start Lab |
| 289 | 📖 Dimensionality Reduction with Pipeline and GridSea... | ★☆☆ | Start Lab |
| 290 | 📖 Detection Error Tradeoff Curve | ★☆☆ | Start Lab |
| 291 | 📖 Precision-Recall Metric for Imbalanced Classificat... | ★☆☆ | Start Lab |
| 292 | 📖 Column Transformer with Mixed Types | ★☆☆ | Start Lab |
| 293 | 📖 Digit Classification with RBM Features | ★☆☆ | Start Lab |
| 294 | 📖 Semi-Supervised Text Classification | ★☆☆ | Start Lab |
| 295 | 📖 Using Set_output API | ★☆☆ | Start Lab |
| 296 | 📖 Feature Discretization for Classification | ★☆☆ | Start Lab |
| 297 | 📖 Text Document Classification | ★☆☆ | Start Lab |
| 298 | 📖 Scikit-Learn Iterative Imputer | ★☆☆ | Start Lab |
| 299 | 📖 Manifold Learning on Handwritten Digits | ★☆☆ | Start Lab |
| 300 | 📖 Constructing Scikit-Learn Pipelines | ★☆☆ | Start Lab |
| 301 | 📖 Feature Scaling in Machine Learning | ★☆☆ | Start Lab |
| 302 | 📖 Pipelines and Composite Estimators | ★☆☆ | Start Lab |
| 303 | 📖 Scikit-Learn Classifier Comparison | ★☆☆ | Start Lab |
## More
- 🔗 [Machine Learning Programming Courses](https://github.com/labex-labs/awesome-programming-courses)
- 🔗 [Machine Learning Programming Projects](https://github.com/labex-labs/awesome-programming-projects)
- 🔗 [Machine Learning Free Tutorials](https://github.com/labex-labs/ml-free-tutorials)