In this course, You will practice more labs of scikit-learn. This will help you to master the skills more deeply.
Index | Name | Difficulty | Practice |
---|---|---|---|
001 | π― Mastering Decision Trees | β ββ | Start Challenge |
002 | π― Understanding Validation Curves | β ββ | Start Challenge |
003 | π― Clustering and Insights | β ββ | Start Challenge |
004 | π― Mastering naive bayes | β ββ | Start Challenge |
005 | π― Mastering Linear Regression | β ββ | Start Challenge |
006 | π― Predicting Flower Types with Nearest Neighbors | β ββ | Start Challenge |
007 | π― Understanding Metrics and Scoring | β ββ | Start Challenge |
008 | π Manifold Learning on Spherical Data | β ββ | Start Lab |
009 | π Faces Dataset Decompositions | β ββ | Start Lab |
010 | π Random Classification Dataset Plotting | β ββ | Start Lab |
011 | π Multilabel Dataset Generation with Scikit-Learn | β ββ | Start Lab |
012 | π Swiss Roll and Swiss-Hole Reduction | β ββ | Start Lab |
013 | π Scikit-Learn Libsvm GUI | β ββ | Start Lab |
014 | π Vector Quantization With KBinsDiscretizer | β ββ | Start Lab |
015 | π Hierarchical Clustering With Scikit-Learn | β ββ | Start Lab |
016 | π Transforming the Prediction Target | β ββ | Start Lab |
017 | π Feature Agglomeration for High-Dimensional Data | β ββ | Start Lab |
018 | π Species Distribution Modeling | β ββ | Start Lab |
019 | π Feature Extraction with Scikit-Learn | β ββ | Start Lab |
020 | π Comparison of F-Test and Mutual Information | β ββ | Start Lab |
021 | π Data Scaling and Transformation | β ββ | Start Lab |
022 | π Curve Fitting With Bayesian Ridge Regression | β ββ | Start Lab |
023 | π Demonstrating KBinsDiscretizer Strategies | β ββ | Start Lab |
024 | π Lasso and Elastic Net | β ββ | Start Lab |
025 | π Logistic Regression Model | β ββ | Start Lab |
026 | π Joint Feature Selection With Multi-Task Lasso | β ββ | Start Lab |
027 | π Applying Regularization Techniques with SGD | β ββ | Start Lab |
028 | π Theil-Sen Regression with Python Scikit-Learn | β ββ | Start Lab |
029 | π Compressive Sensing Image Reconstruction | β ββ | Start Lab |
030 | π FeatureHasher and DictVectorizer Comparison | β ββ | Start Lab |
031 | π Decision Tree Regression | β ββ | Start Lab |
032 | π Multi-Output Decision Tree Regression | β ββ | Start Lab |
033 | π Precompute Gram Matrix for ElasticNet | β ββ | Start Lab |
034 | π Plot Huber vs Ridge | β ββ | Start Lab |
035 | π Simple 1D Kernel Density Estimation | β ββ | Start Lab |
036 | π Scikit-Learn Lasso Regression | β ββ | Start Lab |
037 | π Local Outlier Factor for Novelty Detection | β ββ | Start Lab |
038 | π Outlier Detection With LOF | β ββ | Start Lab |
039 | π Sparse Signal Recovery With Orthogonal Matching Pu... | β ββ | Start Lab |
040 | π Plot SGD Separating Hyperplane | β ββ | Start Lab |
041 | π Density Estimation Using Kernel Density | β ββ | Start Lab |
042 | π Exploring K-Means Clustering with Python | β ββ | Start Lab |
043 | π Agglomerative Clustering on Digits Dataset | β ββ | Start Lab |
044 | π Step-by-Step Logistic Regression | β ββ | Start Lab |
045 | π OPTICS Clustering Algorithm | β ββ | Start Lab |
046 | π Biclustering in Scikit-Learn | β ββ | Start Lab |
047 | π Empirical Evaluation of K-Means Initialization | β ββ | Start Lab |
048 | π Regularization Path of L1- Logistic Regression | β ββ | Start Lab |
049 | π Neighborhood Components Analysis | β ββ | Start Lab |
050 | π Kernel Density Estimate of Species Distributions | β ββ | Start Lab |
051 | π Support Vector Regression | β ββ | Start Lab |
052 | π Affinity Propagation Clustering | β ββ | Start Lab |
053 | π Hierarchical Clustering Dendrogram | β ββ | Start Lab |
054 | π Comparing BIRCH and MiniBatchKMeans | β ββ | Start Lab |
055 | π Bisecting K-Means and Regular K-Means Performance ... | β ββ | Start Lab |
056 | π Comparing Clustering Algorithms | β ββ | Start Lab |
057 | π Demo of HDBSCAN Clustering Algorithm | β ββ | Start Lab |
058 | π Mean-Shift Clustering Algorithm | β ββ | Start Lab |
059 | π Neural Network Models | β ββ | Start Lab |
060 | π Unsupervised Clustering with K-Means | β ββ | Start Lab |
061 | π Random Forest OOB Error Estimation | β ββ | Start Lab |
062 | π Pixel Importances With Parallel Forest of Trees | β ββ | Start Lab |
063 | π Gaussian Process Classification on Iris Dataset | β ββ | Start Lab |
064 | π Gaussian Process Classification | β ββ | Start Lab |
065 | π Gaussian Process Classification on XOR Dataset | β ββ | Start Lab |
066 | π Gaussian Process Regression | β ββ | Start Lab |
067 | π Gaussian Process Regression | β ββ | Start Lab |
068 | π Gaussian Process Regression: Kernels | β ββ | Start Lab |
069 | π Image Segmentation With Hierarchical Clustering | β ββ | Start Lab |
070 | π Color Quantization Using K-Means | β ββ | Start Lab |
071 | π Plot Dict Face Patches | β ββ | Start Lab |
072 | π Gaussian Processes on Discrete Data Structures | β ββ | Start Lab |
073 | π Spectral Clustering for Image Segmentation | β ββ | Start Lab |
074 | π Model-Based and Sequential Feature Selection | β ββ | Start Lab |
075 | π SVM Tie Breaking | β ββ | Start Lab |
076 | π Cross-Validation on Digits Dataset | β ββ | Start Lab |
077 | π Early Stopping of Gradient Boosting | β ββ | Start Lab |
078 | π Machine Learning Cross-Validation with Python | β ββ | Start Lab |
079 | π Plot GPR Co2 | β ββ | Start Lab |
080 | π Linear Regression Example | β ββ | Start Lab |
081 | π Pairwise Metrics and Kernels in Scikit-Learn | β ββ | Start Lab |
082 | π Compare Cross Decomposition Methods | β ββ | Start Lab |
083 | π Discretizing Continuous Features With KBinsDiscret... | β ββ | Start Lab |
084 | π Boosted Decision Tree Regression | β ββ | Start Lab |
085 | π Bias-Variance Decomposition With Bagging | β ββ | Start Lab |
086 | π Scikit-Learn Elastic-Net Regression Model | β ββ | Start Lab |
087 | π Plot Agglomerative Clustering | β ββ | Start Lab |
088 | π Map Data to a Normal Distribution | β ββ | Start Lab |
089 | π Nearest Neighbors Classification | β ββ | Start Lab |
090 | π SVM Classification Using Custom Kernel | β ββ | Start Lab |
091 | π SVM Classifier on Iris Dataset | β ββ | Start Lab |
092 | π Recursive Feature Elimination | β ββ | Start Lab |
093 | π Diabetes Prediction Using Voting Regressor | β ββ | Start Lab |
094 | π Plot Forest Iris | β ββ | Start Lab |
095 | π Cross-Validation With Linear Models | β ββ | Start Lab |
096 | π Text Classification Using Out-of-Core Learning | β ββ | Start Lab |
097 | π Hierarchical Clustering With Connectivity Constrai... | β ββ | Start Lab |
098 | π Imputation of Missing Values | β ββ | Start Lab |
099 | π SVM: Maximum Margin Separating Hyperplane | β ββ | Start Lab |
100 | π SVM for Unbalanced Classes | β ββ | Start Lab |
101 | π Kernel Approximation Techniques in Scikit-Learn | β ββ | Start Lab |
102 | π Blind Source Separation | β ββ | Start Lab |
103 | π Independent Component Analysis with FastICA and PC... | β ββ | Start Lab |
104 | π Iris Flower Classification with Scikit-learn | β ββ | Start Lab |
105 | π Principal Components Analysis | β ββ | Start Lab |
106 | π Hyperparameter Optimization: Randomized Search vs ... | β ββ | Start Lab |
107 | π Sparse Coding With Precomputed Dictionary | β ββ | Start Lab |
108 | π Wikipedia PageRank With Randomized SVD | β ββ | Start Lab |
109 | π Decomposing Signals in Components | β ββ | Start Lab |
110 | π Validation Curves: Plotting Scores to Evaluate Mod... | β ββ | Start Lab |
111 | π Post Pruning Decision Trees | β ββ | Start Lab |
112 | π Comparison of Covariance Estimators | β ββ | Start Lab |
113 | π Robust Covariance Estimation and Mahalanobis Dista... | β ββ | Start Lab |
114 | π Ridge Regression for Linear Modeling | β ββ | Start Lab |
115 | π Robust Covariance Estimation in Python | β ββ | Start Lab |
116 | π Comparing Online Solvers for Handwritten Digit Cla... | β ββ | Start Lab |
117 | π Decision Tree Analysis | β ββ | Start Lab |
118 | π Class Probabilities With VotingClassifier | β ββ | Start Lab |
119 | π Covariance Matrix Estimation with Scikit-Learn | β ββ | Start Lab |
120 | π Preprocessing Techniques in Scikit-Learn | β ββ | Start Lab |
121 | π Agglomerative Clustering Metrics | β ββ | Start Lab |
122 | π Logistic Regression Classifier on Iris Dataset | β ββ | Start Lab |
123 | π Scikit-Learn Multi-Class SGD Classifier | β ββ | Start Lab |
124 | π Manifold Learning with Scikit-Learn | β ββ | Start Lab |
125 | π Comparing Linear Bayesian Regressors | β ββ | Start Lab |
126 | π Incremental Principal Component Analysis on Iris D... | β ββ | Start Lab |
127 | π Lasso Model Selection | β ββ | Start Lab |
128 | π Model Selection for Lasso Regression | β ββ | Start Lab |
129 | π Linear and Quadratic Discriminant Analysis | β ββ | Start Lab |
130 | π Plot Concentration Prior | β ββ | Start Lab |
131 | π Sparse Inverse Covariance Estimation | β ββ | Start Lab |
132 | π Gaussian Mixture Models | β ββ | Start Lab |
133 | π Plot Forest Hist Grad Boosting Comparison | β ββ | Start Lab |
134 | π Clustering Analysis With Silhouette Method | β ββ | Start Lab |
135 | π Plot Multinomial and One-vs-Rest Logistic Regressi... | β ββ | Start Lab |
136 | π Comparing K-Means and MiniBatchKMeans | β ββ | Start Lab |
137 | π Nearest Centroid Classification | β ββ | Start Lab |
138 | π Spectral Biclustering Algorithm | β ββ | Start Lab |
139 | π Spectral Co-Clustering Algorithm | β ββ | Start Lab |
140 | π Permutation Feature Importance | β ββ | Start Lab |
141 | π Probabilistic Predictions With Gaussian Process Cl... | β ββ | Start Lab |
142 | π Decision Trees on Iris Dataset | β ββ | Start Lab |
143 | π Nested Cross-Validation for Model Selection | β ββ | Start Lab |
144 | π Permutation Test Score for Classification | β ββ | Start Lab |
145 | π Recursive Feature Elimination With Cross-Validatio... | β ββ | Start Lab |
146 | π MNIST Multinomial Logistic Regression | β ββ | Start Lab |
147 | π Scaling Regularization Parameter for SVMs | β ββ | Start Lab |
148 | π Plotting Validation Curves | β ββ | Start Lab |
149 | π Isotonic Regression with Scikit-Learn | β ββ | Start Lab |
150 | π Tuning Hyperparameters of an Estimator | β ββ | Start Lab |
151 | π Digits Classification using Scikit-Learn | β ββ | Start Lab |
152 | π Gradient Boosting Monotonic Constraints | β ββ | Start Lab |
153 | π Revealing Iris Dataset Structure via Factor Analys... | β ββ | Start Lab |
154 | π Feature Selection with Scikit-Learn | β ββ | Start Lab |
155 | π Scikit-Learn Confusion Matrix | β ββ | Start Lab |
156 | π Recognizing Hand-Written Digits | β ββ | Start Lab |
157 | π Gradient Boosting Regularization | β ββ | Start Lab |
158 | π Plot Topics Extraction With NMF Lda | β ββ | Start Lab |
159 | π DBSCAN Clustering Algorithm | β ββ | Start Lab |
160 | π Gaussian Mixture Model Initialization Methods | β ββ | Start Lab |
161 | π Active Learning Withel Propagation | β ββ | Start Lab |
162 | π Document Biclustering Using Spectral Co-Clustering... | β ββ | Start Lab |
163 | π Partial Dependence and Individual Conditional Expe... | β ββ | Start Lab |
164 | π ROC With Cross Validation | β ββ | Start Lab |
165 | π Label Propagation Learning | β ββ | Start Lab |
166 | π Ensemble Methods Exploration with Scikit-Learn | β ββ | Start Lab |
167 | π Multi-Class AdaBoosted Decision Trees | β ββ | Start Lab |
168 | π Isotonic Regression with Scikit-Learn | β ββ | Start Lab |
169 | π Sparse Signal Regression With L1-Based Models | β ββ | Start Lab |
170 | π Plotting Learning Curves | β ββ | Start Lab |
171 | π Non-Negative Least Squares Regression | β ββ | Start Lab |
172 | π Quantile Regression with Scikit-Learn | β ββ | Start Lab |
173 | π Semi-Supervised Learning Algorithms | β ββ | Start Lab |
174 | π Outlier Detection with Scikit-Learn | β ββ | Start Lab |
175 | π Categorical Data Transformation using TargetEncode... | β ββ | Start Lab |
176 | π Underfitting and Overfitting | β ββ | Start Lab |
177 | π AdaBoost Decision Stump Classification | β ββ | Start Lab |
178 | π Shrinkage Covariance Estimation | β ββ | Start Lab |
179 | π Plotting Predictions With Cross-Validation | β ββ | Start Lab |
180 | π Exploring K-Means Clustering Assumptions | β ββ | Start Lab |
181 | π Visualize High-Dimensional Data with MDS | β ββ | Start Lab |
182 | π Robust Linear Estimator Fitting | β ββ | Start Lab |
183 | π Evaluating Machine Learning Model Quality | β ββ | Start Lab |
184 | π Caching Nearest Neighbors | β ββ | Start Lab |
185 | π Optimizing Model Hyperparameters with GridSearchCV | β ββ | Start Lab |
186 | π Exploring Johnson-Lindenstrauss Lemma with Random ... | β ββ | Start Lab |
187 | π Principal Component Analysis with Kernel PCA | β ββ | Start Lab |
188 | π Outlier Detection With Scikit-Learn | β ββ | Start Lab |
189 | π Digit Dataset Analysis | β ββ | Start Lab |
190 | π Gaussian Mixture Model Covariances | β ββ | Start Lab |
191 | π Gaussian Mixture Model Selection | β ββ | Start Lab |
192 | π Plot Grid Search Digits | β ββ | Start Lab |
193 | π Multiclass ROC Evaluation With Scikit-Learn | β ββ | Start Lab |
194 | π Semi-Supervised Classifiers on the Iris Dataset | β ββ | Start Lab |
195 | π Gradient Boosting Out-of-Bag Estimates | β ββ | Start Lab |
196 | π Text Feature Extraction and Evaluation | β ββ | Start Lab |
197 | π Image Denoising With Kernel PCA | β ββ | Start Lab |
198 | π Anomaly Detection With Isolation Forest | β ββ | Start Lab |
199 | π Hashing Feature Transformation | β ββ | Start Lab |
200 | π Explicit Feature Map Approximation for RBF Kernels | β ββ | Start Lab |
201 | π Plotting Classification Probability | β ββ | Start Lab |
202 | π Probability Calibration for 3-Class Classification | β ββ | Start Lab |
203 | π Plot Compare GPR KRR | β ββ | Start Lab |
204 | π Feature Transformations With Ensembles of Trees | β ββ | Start Lab |
205 | π Feature Importance With Random Forest | β ββ | Start Lab |
206 | π Multi-Layer Perceptron Regularization | β ββ | Start Lab |
207 | π Discrete Versus Real AdaBoost | β ββ | Start Lab |
208 | π Scikit-Learn MLPClassifier: Stochastic Learning St... | β ββ | Start Lab |
209 | π Kernel Density Estimation | β ββ | Start Lab |
210 | π Plot Pca vs Lda | β ββ | Start Lab |
211 | π Univariate Feature Selection | β ββ | Start Lab |
212 | π Early Stopping of Stochastic Gradient Descent | β ββ | Start Lab |
213 | π Feature Selection for SVC on Iris Dataset | β ββ | Start Lab |
214 | π Approximate Nearest Neighbors in TSNE | β ββ | Start Lab |
215 | π K-Means Clustering on Handwritten Digits | β ββ | Start Lab |
216 | π Linear Discriminant Analysis for Classification | β ββ | Start Lab |
217 | π Plot Sgdocsvm vs Ocsvm | β ββ | Start Lab |
218 | π Plot Kernel Ridge Regression | β ββ | Start Lab |
219 | π Polynomial Kernel Approximation With Scikit-Learn | β ββ | Start Lab |
220 | π Scikit-Learn Visualization API | β ββ | Start Lab |
221 | π Plot Random Forest Regression Multioutput | β ββ | Start Lab |
222 | π Multiclass Sparse Logistic Regression | β ββ | Start Lab |
223 | π Creating Visualizations With Display Objects | β ββ | Start Lab |
224 | π Iris Flower Classification using Voting Classifier | β ββ | Start Lab |
225 | π Comparison Between Grid Search and Successive Halv... | β ββ | Start Lab |
226 | π Plot Nca Classification | β ββ | Start Lab |
227 | π Transforming Target for Linear Regression | β ββ | Start Lab |
228 | π Successive Halving Iterations | β ββ | Start Lab |
229 | π Plot Digits Pipe | β ββ | Start Lab |
230 | π Scikit-Learn Estimators and Pipelines | β ββ | Start Lab |
231 | π Classify Handwritten Digits with MLP Classifier | β ββ | Start Lab |
232 | π Gradient Boosting With Categorical Features | β ββ | Start Lab |
233 | π Plot Pca vs Fa Model Selection | β ββ | Start Lab |
234 | π Building Machine Learning Pipelines with Scikit-Le... | β ββ | Start Lab |
235 | π Balance Model Complexity and Cross-Validated Score | β ββ | Start Lab |
236 | π Text Document Classification | β ββ | Start Lab |
237 | π Digit Classification With RBM Features | β ββ | Start Lab |
238 | π Comparison of Calibration of Classifiers | β ββ | Start Lab |
239 | π Face Recognition With Eigenfaces and SVMs | β ββ | Start Lab |
240 | π Concatenating Multiple Feature Extraction Methods | β ββ | Start Lab |
241 | π Detection Error Tradeoff Curve | β ββ | Start Lab |
242 | π Dimensionality Reduction With Pipeline and GridSea... | β ββ | Start Lab |
243 | π Effect of Varying Threshold for Self-Training | β ββ | Start Lab |
244 | π Probability Calibration Curves | β ββ | Start Lab |
245 | π Class Likelihood Ratios to Measure Classification ... | β ββ | Start Lab |
246 | π Plot PCR vs PLS | β ββ | Start Lab |
247 | π Multi-Label Document Classification | β ββ | Start Lab |
248 | π Column Transformer With Mixed Types | β ββ | Start Lab |
249 | π Using Set_output API | β ββ | Start Lab |
250 | π Anomaly Detection Algorithms Comparison | β ββ | Start Lab |
251 | π Multiclass and Multioutput Algorithms | β ββ | Start Lab |
252 | π Precision-Recall Metric for Imbalanced Classificat... | β ββ | Start Lab |
253 | π Semi-Supervised Text Classification | β ββ | Start Lab |
254 | π Impute Missing Data | β ββ | Start Lab |
255 | π Feature Discretization for Classification | β ββ | Start Lab |
256 | π Pipelines and Composite Estimators | β ββ | Start Lab |
257 | π Feature Scaling in Machine Learning | β ββ | Start Lab |
258 | π Constructing Scikit-Learn Pipelines | β ββ | Start Lab |
259 | π Scikit-Learn Iterative Imputer | β ββ | Start Lab |
260 | π Manifold Learning on Handwritten Digits | β ββ | Start Lab |
261 | π Scikit-Learn Classifier Comparison | β ββ | Start Lab |