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scikit-learn Free Tutorials

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 structured roadmap to understand ML algorithms, model selection, and evaluation. Through hands-on, non-video courses and practical exercises in a data science playground, you'll gain real-world experience in implementing machine learning solutions.

Index Name Difficulty Tutorial Link
001 📖 Linear Models in Scikit-Learn ★☆☆ 🔗 View
002 📖 Discriminant Analysis Classifiers Explained ★☆☆ 🔗 View
003 📖 Exploring Scikit-Learn Datasets and Estimators ★☆☆ 🔗 View
004 📖 Kernel Ridge Regression ★☆☆ 🔗 View
005 📖 Supervised Learning with Scikit-Learn ★☆☆ 🔗 View
006 📖 Model Selection: Choosing Estimators and Their Parameters ★☆☆ 🔗 View
007 📖 Supervised Learning with Support Vectors ★☆☆ 🔗 View
008 📖 Exploring Scikit-Learn SGD Classifiers ★☆☆ 🔗 View
009 📖 Unsupervised Learning: Seeking Representations of the Data ★☆☆ 🔗 View
010 📖 Implementing Stochastic Gradient Descent ★☆☆ 🔗 View
011 📖 Working with Text Data ★☆☆ 🔗 View
012 📖 Gaussian Process Regression and Classification ★☆☆ 🔗 View
013 📖 Dimensional Reduction with PLS Algorithms ★☆☆ 🔗 View
014 📖 Naive Bayes Example ★☆☆ 🔗 View
015 📖 Decision Tree Classification with Scikit-Learn ★☆☆ 🔗 View
016 📖 Scikit-Learn Classifier Comparison ★☆☆ 🔗 View
017 📖 Text Document Classification ★☆☆ 🔗 View
018 📖 Feature Discretization for Classification ★☆☆ 🔗 View
019 📖 Pipelines and Composite Estimators ★☆☆ 🔗 View
020 📖 Feature Scaling in Machine Learning ★☆☆ 🔗 View
021 📖 Constructing Scikit-Learn Pipelines ★☆☆ 🔗 View
022 📖 Scikit-Learn Iterative Imputer ★☆☆ 🔗 View
023 📖 Manifold Learning on Handwritten Digits ★☆☆ 🔗 View
024 📖 Digit Classification with RBM Features ★☆☆ 🔗 View
025 📖 Column Transformer with Mixed Types ★☆☆ 🔗 View
026 📖 Using Set_output API ★☆☆ 🔗 View
027 📖 Semi-Supervised Text Classification ★☆☆ 🔗 View
028 📖 Comparison of Calibration of Classifiers ★☆☆ 🔗 View
029 📖 Detection Error Tradeoff Curve ★☆☆ 🔗 View
030 📖 Dimensionality Reduction with Pipeline and GridSearchCV ★☆☆ 🔗 View
031 📖 Probability Calibration Curves ★☆☆ 🔗 View
032 📖 Anomaly Detection Algorithms Comparison ★☆☆ 🔗 View
033 📖 Precision-Recall Metric for Imbalanced Classification ★☆☆ 🔗 View
034 📖 Univariate Feature Selection ★☆☆ 🔗 View
035 📖 Feature Selection for SVC on Iris Dataset ★☆☆ 🔗 View
036 📖 Approximate Nearest Neighbors in TSNE ★☆☆ 🔗 View
037 📖 Creating Visualizations with Display Objects ★☆☆ 🔗 View
038 📖 Transforming Target for Linear Regression ★☆☆ 🔗 View
039 📖 Gradient Boosting with Categorical Features ★☆☆ 🔗 View
040 📖 Building Machine Learning Pipelines with Scikit-Learn ★☆☆ 🔗 View
041 📖 Face Recognition with Eigenfaces and SVMs ★☆☆ 🔗 View
042 📖 Concatenating Multiple Feature Extraction Methods ★☆☆ 🔗 View
043 📖 Class Likelihood Ratios to Measure Classification Performance ★☆☆ 🔗 View
044 📖 Plot PCR vs PLS ★☆☆ 🔗 View
045 📖 Multiclass and Multioutput Algorithms ★☆☆ 🔗 View
046 📖 Impute Missing Data ★☆☆ 🔗 View
047 📖 MNIST Multinomial Logistic Regression ★☆☆ 🔗 View
048 📖 Outlier Detection Using Scikit-Learn Algorithms ★☆☆ 🔗 View
049 📖 Multiclass ROC Evaluation with Scikit-Learn ★☆☆ 🔗 View
050 📖 Text Feature Extraction and Evaluation ★☆☆ 🔗 View
051 📖 Feature Transformations with Ensembles of Trees ★☆☆ 🔗 View
052 📖 Multi-Layer Perceptron Regularization ★☆☆ 🔗 View
053 📖 K-Means Clustering on Handwritten Digits ★☆☆ 🔗 View
054 📖 Polynomial Kernel Approximation with Scikit-Learn ★☆☆ 🔗 View
055 📖 Scikit-Learn Visualization API ★☆☆ 🔗 View
056 📖 Iris Flower Classification using Voting Classifier ★☆☆ 🔗 View
057 📖 Plot Nca Classification ★☆☆ 🔗 View
058 📖 Plot Digits Pipe ★☆☆ 🔗 View
059 📖 Scikit-Learn Estimators and Pipelines ★☆☆ 🔗 View
060 📖 Balance Model Complexity and Cross-Validated Score ★☆☆ 🔗 View
061 📖 Effect of Varying Threshold for Self-Training ★☆☆ 🔗 View
062 📖 Multi-Label Document Classification ★☆☆ 🔗 View
063 📖 Text Classification Using Out-of-Core Learning ★☆☆ 🔗 View
064 📖 Comparing Linear Bayesian Regressors ★☆☆ 🔗 View
065 📖 Lasso Model Selection ★☆☆ 🔗 View
066 📖 Model Selection for Lasso Regression ★☆☆ 🔗 View
067 📖 Recursive Feature Elimination with Cross-Validation ★☆☆ 🔗 View
068 📖 Feature Selection with Scikit-Learn ★☆☆ 🔗 View
069 📖 DBSCAN Clustering Algorithm ★☆☆ 🔗 View
070 📖 Document Biclustering Using Spectral Co-Clustering Algorithm ★☆☆ 🔗 View
071 📖 Ensemble Methods Exploration with Scikit-Learn ★☆☆ 🔗 View
072 📖 Multi-Class AdaBoosted Decision Trees ★☆☆ 🔗 View
073 📖 Plotting Learning Curves ★☆☆ 🔗 View
074 📖 Categorical Data Transformation using TargetEncoder ★☆☆ 🔗 View
075 📖 Underfitting and Overfitting ★☆☆ 🔗 View
076 📖 AdaBoost Decision Stump Classification ★☆☆ 🔗 View
077 📖 Plotting Predictions with Cross-Validation ★☆☆ 🔗 View
078 📖 Robust Linear Estimator Fitting ★☆☆ 🔗 View
079 📖 Evaluating Machine Learning Model Quality ★☆☆ 🔗 View
080 📖 Caching Nearest Neighbors ★☆☆ 🔗 View
081 📖 Optimizing Model Hyperparameters with GridSearchCV ★☆☆ 🔗 View
082 📖 Gradient Boosting Out-of-Bag Estimates ★☆☆ 🔗 View
083 📖 Image Denoising with Kernel PCA ★☆☆ 🔗 View
084 📖 Hashing Feature Transformation ★☆☆ 🔗 View
085 📖 Plotting Classification Probability ★☆☆ 🔗 View
086 📖 Probability Calibration for 3-Class Classification ★☆☆ 🔗 View
087 📖 Feature Importance with Random Forest ★☆☆ 🔗 View
088 📖 Discrete Versus Real AdaBoost ★☆☆ 🔗 View
089 📖 Kernel Density Estimation ★☆☆ 🔗 View
090 📖 Early Stopping of Stochastic Gradient Descent ★☆☆ 🔗 View
091 📖 Plot Sgdocsvm vs Ocsvm ★☆☆ 🔗 View
092 📖 Multiclass Sparse Logistic Regression ★☆☆ 🔗 View
093 📖 Successive Halving Iterations ★☆☆ 🔗 View
094 📖 Classify Handwritten Digits with MLP Classifier ★☆☆ 🔗 View
095 📖 Color Quantization Using K-Means ★☆☆ 🔗 View
096 📖 Model-Based and Sequential Feature Selection ★☆☆ 🔗 View
097 📖 Discretizing Continuous Features with KBinsDiscretizer ★☆☆ 🔗 View
098 📖 Recursive Feature Elimination ★☆☆ 🔗 View
099 📖 Diabetes Prediction Using Voting Regressor ★☆☆ 🔗 View
100 📖 Plot Forest Iris ★☆☆ 🔗 View
101 📖 Hierarchical Clustering with Connectivity Constraints ★☆☆ 🔗 View
102 📖 Hyperparameter Optimization: Randomized Search vs Grid Search ★☆☆ 🔗 View
103 📖 Validation Curves: Plotting Scores to Evaluate Models ★☆☆ 🔗 View
104 📖 Post Pruning Decision Trees ★☆☆ 🔗 View
105 📖 Ridge Regression for Linear Modeling ★☆☆ 🔗 View
106 📖 Comparing Online Solvers for Handwritten Digit Classification ★☆☆ 🔗 View
107 📖 Decision Tree Analysis ★☆☆ 🔗 View
108 📖 Class Probabilities with VotingClassifier ★☆☆ 🔗 View
109 📖 Plot Forest Hist Grad Boosting Comparison ★☆☆ 🔗 View
110 📖 Clustering Analysis with Silhouette Method ★☆☆ 🔗 View
111 📖 Plot Multinomial and One-vs-Rest Logistic Regression ★☆☆ 🔗 View
112 📖 Comparing K-Means and MiniBatchKMeans ★☆☆ 🔗 View
113 📖 Spectral Biclustering Algorithm ★☆☆ 🔗 View
114 📖 Spectral Co-Clustering Algorithm ★☆☆ 🔗 View
115 📖 Permutation Feature Importance ★☆☆ 🔗 View
116 📖 Decision Trees on Iris Dataset ★☆☆ 🔗 View
117 📖 Nested Cross-Validation for Model Selection ★☆☆ 🔗 View
118 📖 Permutation Test Score for Classification ★☆☆ 🔗 View
119 📖 Scaling Regularization Parameter for SVMs ★☆☆ 🔗 View
120 📖 Plotting Validation Curves ★☆☆ 🔗 View
121 📖 Tuning Hyperparameters of an Estimator ★☆☆ 🔗 View
122 📖 Digits Classification using Scikit-Learn ★☆☆ 🔗 View
123 📖 Revealing Iris Dataset Structure via Factor Analysis ★☆☆ 🔗 View
124 📖 Plot Topics Extraction with NMF Lda ★☆☆ 🔗 View
125 📖 Gaussian Mixture Model Initialization Methods ★☆☆ 🔗 View
126 📖 Partial Dependence and Individual Conditional Expectation ★☆☆ 🔗 View
127 📖 ROC with Cross Validation ★☆☆ 🔗 View
128 📖 Nonparametric Isotonic Regression with Scikit-Learn ★☆☆ 🔗 View
129 📖 Sparse Signal Regression with L1-Based Models ★☆☆ 🔗 View
130 📖 Non-Negative Least Squares Regression ★☆☆ 🔗 View
131 📖 Quantile Regression with Scikit-Learn ★☆☆ 🔗 View
132 📖 Detecting Outliers in Wine Data ★☆☆ 🔗 View
133 📖 Exploring K-Means Clustering Assumptions ★☆☆ 🔗 View
134 📖 Exploring Johnson-Lindenstrauss Lemma with Random Projections ★☆☆ 🔗 View
135 📖 Principal Component Analysis with Kernel PCA ★☆☆ 🔗 View
136 📖 Digit Dataset Analysis ★☆☆ 🔗 View
137 📖 Plot Grid Search Digits ★☆☆ 🔗 View
138 📖 Anomaly Detection with Isolation Forest ★☆☆ 🔗 View
139 📖 Plot Compare GPR KRR ★☆☆ 🔗 View
140 📖 Scikit-Learn MLPClassifier: Stochastic Learning Strategies ★☆☆ 🔗 View
141 📖 Linear Discriminant Analysis for Classification ★☆☆ 🔗 View
142 📖 Plot Kernel Ridge Regression ★☆☆ 🔗 View
143 📖 Plot Random Forest Regression Multioutput ★☆☆ 🔗 View
144 📖 Comparison Between Grid Search and Successive Halving ★☆☆ 🔗 View
145 📖 Plot Pca vs Fa Model Selection ★☆☆ 🔗 View
146 📖 Species Distribution Modeling ★☆☆ 🔗 View
147 📖 Data Scaling and Transformation ★☆☆ 🔗 View
148 📖 Demonstrating KBinsDiscretizer Strategies ★☆☆ 🔗 View
149 📖 FeatureHasher and DictVectorizer Comparison ★☆☆ 🔗 View
150 📖 Precompute Gram Matrix for ElasticNet ★☆☆ 🔗 View
151 📖 Plot Huber vs Ridge ★☆☆ 🔗 View
152 📖 Scikit-Learn Lasso Regression ★☆☆ 🔗 View
153 📖 Sparse Signal Recovery with Orthogonal Matching Pursuit ★☆☆ 🔗 View
154 📖 Plot SGD Separating Hyperplane ★☆☆ 🔗 View
155 📖 Step-by-Step Logistic Regression ★☆☆ 🔗 View
156 📖 Empirical Evaluation of K-Means Initialization ★☆☆ 🔗 View
157 📖 Neighborhood Components Analysis ★☆☆ 🔗 View
158 📖 Kernel Density Estimate of Species Distributions ★☆☆ 🔗 View
159 📖 Affinity Propagation Clustering ★☆☆ 🔗 View
160 📖 Hierarchical Clustering Dendrogram ★☆☆ 🔗 View
161 📖 Comparing BIRCH and MiniBatchKMeans ★☆☆ 🔗 View
162 📖 Bisecting K-Means and Regular K-Means Performance Comparison ★☆☆ 🔗 View
163 📖 Comparing Clustering Algorithms ★☆☆ 🔗 View
164 📖 Demo of HDBSCAN Clustering Algorithm ★☆☆ 🔗 View
165 📖 Mean-Shift Clustering Algorithm ★☆☆ 🔗 View
166 📖 Unsupervised Clustering with K-Means ★☆☆ 🔗 View
167 📖 Random Forest OOB Error Estimation ★☆☆ 🔗 View
168 📖 Pixel Importances with Parallel Forest of Trees ★☆☆ 🔗 View
169 📖 Image Segmentation with Hierarchical Clustering ★☆☆ 🔗 View
170 📖 Plot Dict Face Patches ★☆☆ 🔗 View
171 📖 Gaussian Processes on Discrete Data Structures ★☆☆ 🔗 View
172 📖 Spectral Clustering for Image Segmentation ★☆☆ 🔗 View
173 📖 SVM Tie Breaking ★☆☆ 🔗 View
174 📖 Plot GPR Co2 ★☆☆ 🔗 View
175 📖 Boosted Decision Tree Regression ★☆☆ 🔗 View
176 📖 Bias-Variance Decomposition with Bagging ★☆☆ 🔗 View
177 📖 Scikit-Learn Elastic-Net Regression Model ★☆☆ 🔗 View
178 📖 Plot Agglomerative Clustering ★☆☆ 🔗 View
179 📖 Map Data to a Normal Distribution ★☆☆ 🔗 View
180 📖 Cross-Validation with Linear Models ★☆☆ 🔗 View
181 📖 SVM: Maximum Margin Separating Hyperplane ★☆☆ 🔗 View
182 📖 SVM for Unbalanced Classes ★☆☆ 🔗 View
183 📖 Preprocessing Techniques in Scikit-Learn ★☆☆ 🔗 View
184 📖 Agglomerative Clustering Metrics ★☆☆ 🔗 View
185 📖 Logistic Regression Classifier on Iris Dataset ★☆☆ 🔗 View
186 📖 Scikit-Learn Multi-Class SGD Classifier ★☆☆ 🔗 View
187 📖 Incremental Principal Component Analysis on Iris Dataset ★☆☆ 🔗 View
188 📖 Sparse Inverse Covariance Estimation ★☆☆ 🔗 View
189 📖 Nearest Centroid Classification ★☆☆ 🔗 View
190 📖 Probabilistic Predictions with Gaussian Process Classification ★☆☆ 🔗 View
191 📖 Gradient Boosting Monotonic Constraints ★☆☆ 🔗 View
192 📖 Scikit-Learn Confusion Matrix ★☆☆ 🔗 View
193 📖 Recognizing Hand-Written Digits ★☆☆ 🔗 View
194 📖 Gradient Boosting Regularization ★☆☆ 🔗 View
195 📖 Label Propagation Learning ★☆☆ 🔗 View
196 📖 Semi-Supervised Learning Algorithms ★☆☆ 🔗 View
197 📖 Nonlinear Data Regression Techniques ★☆☆ 🔗 View
198 📖 Prediction for Bitcoin Price ★☆☆ 🔗 View
199 📖 Shrinkage Covariance Estimation ★☆☆ 🔗 View
200 📖 Visualize High-Dimensional Data with MDS ★☆☆ 🔗 View
201 📖 Gaussian Mixture Model Covariances ★☆☆ 🔗 View
202 📖 Gaussian Mixture Model Selection ★☆☆ 🔗 View
203 📖 Semi-Supervised Classifiers on the Iris Dataset ★☆☆ 🔗 View
204 📖 Explicit Feature Map Approximation for RBF Kernels ★☆☆ 🔗 View
205 📖 Plot Pca vs Lda ★☆☆ 🔗 View
206 📖 Manifold Learning on Spherical Data ★☆☆ 🔗 View
207 📖 Faces Dataset Decompositions ★☆☆ 🔗 View
208 📖 Random Classification Dataset Plotting ★☆☆ 🔗 View
209 📖 Multilabel Dataset Generation with Scikit-Learn ★☆☆ 🔗 View
210 📖 Swiss Roll and Swiss-Hole Reduction ★☆☆ 🔗 View
211 📖 Scikit-Learn Libsvm GUI ★☆☆ 🔗 View
212 📖 Vector Quantization with KBinsDiscretizer ★☆☆ 🔗 View
213 📖 Hierarchical Clustering with Scikit-Learn ★☆☆ 🔗 View
214 📖 Transforming the Prediction Target ★☆☆ 🔗 View
215 📖 Feature Agglomeration for High-Dimensional Data ★☆☆ 🔗 View
216 📖 Feature Extraction with Scikit-Learn ★☆☆ 🔗 View
217 📖 Comparison of F-Test and Mutual Information ★☆☆ 🔗 View
218 📖 Curve Fitting with Bayesian Ridge Regression ★☆☆ 🔗 View
219 📖 Lasso and Elastic Net ★☆☆ 🔗 View
220 📖 Logistic Regression Model ★☆☆ 🔗 View
221 📖 Joint Feature Selection with Multi-Task Lasso ★☆☆ 🔗 View
222 📖 Applying Regularization Techniques with SGD ★☆☆ 🔗 View
223 📖 Theil-Sen Regression with Python Scikit-Learn ★☆☆ 🔗 View
224 📖 Compressive Sensing Image Reconstruction ★☆☆ 🔗 View
225 📖 Decision Tree Regression ★☆☆ 🔗 View
226 📖 Multi-Output Decision Tree Regression ★☆☆ 🔗 View
227 📖 Simple 1D Kernel Density Estimation ★☆☆ 🔗 View
228 📖 Local Outlier Factor for Novelty Detection ★☆☆ 🔗 View
229 📖 Outlier Detection with LOF ★☆☆ 🔗 View
230 📖 Density Estimation Using Kernel Density ★☆☆ 🔗 View
231 📖 Exploring K-Means Clustering with Python ★☆☆ 🔗 View
232 📖 Agglomerative Clustering on Digits Dataset ★☆☆ 🔗 View
233 📖 OPTICS Clustering Algorithm ★☆☆ 🔗 View
234 📖 Biclustering in Scikit-Learn ★☆☆ 🔗 View
235 📖 Regularization Path of L1-Logistic Regression ★☆☆ 🔗 View
236 📖 Support Vector Regression ★☆☆ 🔗 View
237 📖 Centroid Based Clustering ★☆☆ 🔗 View
238 📖 Neural Network Models ★☆☆ 🔗 View
239 📖 Gaussian Process Classification on Iris Dataset ★☆☆ 🔗 View
240 📖 Gaussian Process Classification ★☆☆ 🔗 View
241 📖 Gaussian Process Classification on XOR Dataset ★☆☆ 🔗 View
242 📖 Nonlinear Predictive Modeling Using Gaussian Process ★☆☆ 🔗 View
243 📖 Fit Gaussian Process Regression Model ★☆☆ 🔗 View
244 📖 Gaussian Process Regression: Kernels ★☆☆ 🔗 View
245 📖 Spectral Clustering and Other Clustering Methods ★☆☆ 🔗 View
246 📖 Nonlinear Pattern Recognition Techniques ★☆☆ 🔗 View
247 📖 Quickly Select Models with Cross Validation ★☆☆ 🔗 View
248 📖 Cross-Validation on Digits Dataset ★☆☆ 🔗 View
249 📖 Early Stopping of Gradient Boosting ★☆☆ 🔗 View
250 📖 Machine Learning Cross-Validation with Python ★☆☆ 🔗 View
251 📖 Linear Regression Example ★☆☆ 🔗 View
252 📖 Pairwise Metrics and Kernels in Scikit-Learn ★☆☆ 🔗 View
253 📖 Compare Cross Decomposition Methods ★☆☆ 🔗 View
254 📖 Nearest Neighbors Classification ★☆☆ 🔗 View
255 📖 SVM Classification Using Custom Kernel ★☆☆ 🔗 View
256 📖 SVM Classifier on Iris Dataset ★☆☆ 🔗 View
257 📖 Imputation of Missing Values ★☆☆ 🔗 View
258 📖 Decision Tree Classification with Python ★☆☆ 🔗 View
259 📖 Kernel Approximation Techniques in Scikit-Learn ★☆☆ 🔗 View
260 📖 Probabilistic Classification with Naive Bayes ★☆☆ 🔗 View
261 📖 Blind Source Separation ★☆☆ 🔗 View
262 📖 Independent Component Analysis with FastICA and PCA ★☆☆ 🔗 View
263 📖 Iris Flower Classification with Scikit-learn ★☆☆ 🔗 View
264 📖 Principal Components Analysis ★☆☆ 🔗 View
265 📖 Sparse Coding with Precomputed Dictionary ★☆☆ 🔗 View
266 📖 Wikipedia PageRank with Randomized SVD ★☆☆ 🔗 View
267 📖 Decomposing Signals in Components ★☆☆ 🔗 View
268 📖 Comparison of Covariance Estimators ★☆☆ 🔗 View
269 📖 Robust Covariance Estimation and Mahalanobis Distances Relevance ★☆☆ 🔗 View
270 📖 Robust Covariance Estimation in Python ★☆☆ 🔗 View
271 📖 Covariance Matrix Estimation with Scikit-Learn ★☆☆ 🔗 View
272 📖 Manifold Learning with Scikit-Learn ★☆☆ 🔗 View
273 📖 Discriminant Analysis Classification Algorithms ★☆☆ 🔗 View
274 📖 Plot Concentration Prior ★☆☆ 🔗 View
275 📖 Gaussian Mixture Models ★☆☆ 🔗 View
276 📖 Nonlinear Regression with Isotonic ★☆☆ 🔗 View
277 📖 Active Learning Withel Propagation ★☆☆ 🔗 View
278 📖 Bagging and Boosting Method ★☆☆ 🔗 View
279 📖 Hierarchical Clustering Exploration for Clustering ★☆☆ 🔗 View
280 📖 Guide of Tensorflow ★☆☆ 🔗 View
281 📖 Shallow Neural Network Implemented by Tensorflow 2 ★☆☆ 🔗 View
282 📖 Tensorflow 2 Model Saving and Restoring ★☆☆ 🔗 View
283 📖 Train Handwritten Digits Recognition Neural Network ★☆☆ 🔗 View
284 📖 Calculation of Ridge Regression Coefficient ★☆☆ 🔗 View
285 📖 Linear Regression Fundamentals ★☆☆ 🔗 View
286 📖 Logistic Regression Classification with Scikit-Learn ★☆☆ 🔗 View
287 📖 Prediction for Beijing Housing Prices ★☆☆ 🔗 View
288 📖 Density Based Clustering ★☆☆ 🔗 View
289 📖 Image Compression Using Mini Batch K Means ★☆☆ 🔗 View
290 📖 Density-Based Clustering Application ★☆☆ 🔗 View
291 📖 K Nearest Neighbor Algorithm ★☆☆ 🔗 View
292 📖 Ridge Regression and Lasso Regression ★☆☆ 🔗 View
293 📖 Classification of Car Safety Evaluation Dataset ★☆☆ 🔗 View
294 📖 Perceptron and Artificial Neural Network ★☆☆ 🔗 View

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