Sklearn Practice Labs (Deprecated)
Beginner
This course contains lots of labs for Sklearn, each lab is a small Sklearn project with detailed guidance and solutions. You can practice your Sklearn skills by completing these labs, improve your coding skills, and learn how to write clean and efficient code.
sklearnpythondata-science
- Intro
- Syllabus
Scikit-Learn Classifier Comparison
Text Document Classification
Feature Discretization for Classification
Pipelines and Composite Estimators
Feature Scaling in Machine Learning
Constructing Scikit-Learn Pipelines
Scikit-Learn Iterative Imputer
Manifold Learning on Handwritten Digits
Digit Classification with RBM Features
Column Transformer with Mixed Types
Using Set_output API
Semi-Supervised Text Classification
Comparison of Calibration of Classifiers
Detection Error Tradeoff Curve
Dimensionality Reduction with Pipeline and GridSearchCV
Probability Calibration Curves
Anomaly Detection Algorithms Comparison
Precision-Recall Metric for Imbalanced Classification
Univariate Feature Selection
Feature Selection for SVC on Iris Dataset
Approximate Nearest Neighbors in TSNE
Creating Visualizations with Display Objects
Transforming Target for Linear Regression
Gradient Boosting with Categorical Features
Building Machine Learning Pipelines with Scikit-Learn
Face Recognition with Eigenfaces and SVMs
Concatenating Multiple Feature Extraction Methods
Class Likelihood Ratios to Measure Classification Performance
Plot PCR vs PLS
Multiclass and Multioutput Algorithms
Impute Missing Data
MNIST Multinomial Logistic Regression
Outlier Detection Using Scikit-Learn Algorithms
Multiclass ROC Evaluation with Scikit-Learn
Text Feature Extraction and Evaluation
Feature Transformations with Ensembles of Trees
Multi-Layer Perceptron Regularization
K-Means Clustering on Handwritten Digits
Polynomial Kernel Approximation with Scikit-Learn
Scikit-Learn Visualization API
Iris Flower Classification using Voting Classifier
Plot Nca Classification
Plot Digits Pipe
Scikit-Learn Estimators and Pipelines
Balance Model Complexity and Cross-Validated Score
Effect of Varying Threshold for Self-Training
Multi-Label Document Classification
Text Classification Using Out-of-Core Learning
Comparing Linear Bayesian Regressors
Lasso Model Selection
Model Selection for Lasso Regression
Recursive Feature Elimination with Cross-Validation
Feature Selection with Scikit-Learn
DBSCAN Clustering Algorithm
Document Biclustering Using Spectral Co-Clustering Algorithm
Ensemble Methods Exploration with Scikit-Learn
Multi-Class AdaBoosted Decision Trees
Plotting Learning Curves
Categorical Data Transformation using TargetEncoder
Underfitting and Overfitting
AdaBoost Decision Stump Classification
Plotting Predictions with Cross-Validation
Robust Linear Estimator Fitting
Evaluating Machine Learning Model Quality
Caching Nearest Neighbors
Optimizing Model Hyperparameters with GridSearchCV
Gradient Boosting Out-of-Bag Estimates
Image Denoising with Kernel PCA
Hashing Feature Transformation
Plotting Classification Probability
Probability Calibration for 3-Class Classification
Feature Importance with Random Forest
Discrete Versus Real AdaBoost
Kernel Density Estimation
Early Stopping of Stochastic Gradient Descent
Plot Sgdocsvm vs Ocsvm
Multiclass Sparse Logistic Regression
Successive Halving Iterations
Classify Handwritten Digits with MLP Classifier
Color Quantization Using K-Means
Model-Based and Sequential Feature Selection
Discretizing Continuous Features with KBinsDiscretizer
Recursive Feature Elimination
Diabetes Prediction Using Voting Regressor
Plot Forest Iris
Hierarchical Clustering with Connectivity Constraints
Hyperparameter Optimization: Randomized Search vs Grid Search
Validation Curves: Plotting Scores to Evaluate Models
Post Pruning Decision Trees
Ridge Regression for Linear Modeling
Comparing Online Solvers for Handwritten Digit Classification
Decision Tree Analysis
Class Probabilities with VotingClassifier
Plot Forest Hist Grad Boosting Comparison
Clustering Analysis with Silhouette Method
Plot Multinomial and One-vs-Rest Logistic Regression
Comparing K-Means and MiniBatchKMeans
Spectral Biclustering Algorithm
Spectral Co-Clustering Algorithm
Permutation Feature Importance
Decision Trees on Iris Dataset
Nested Cross-Validation for Model Selection
Permutation Test Score for Classification
Scaling Regularization Parameter for SVMs
Plotting Validation Curves
Tuning Hyperparameters of an Estimator
Digits Classification using Scikit-Learn
Revealing Iris Dataset Structure via Factor Analysis
Plot Topics Extraction with NMF Lda
Gaussian Mixture Model Initialization Methods
Partial Dependence and Individual Conditional Expectation
ROC with Cross Validation
Nonparametric Isotonic Regression with Scikit-Learn
Sparse Signal Regression with L1-Based Models
Non-Negative Least Squares Regression
Quantile Regression with Scikit-Learn
Detecting Outliers in Wine Data
Exploring K-Means Clustering Assumptions
Exploring Johnson-Lindenstrauss Lemma with Random Projections
Principal Component Analysis with Kernel PCA
Digit Dataset Analysis
Plot Grid Search Digits
Anomaly Detection with Isolation Forest
Plot Compare GPR KRR
Scikit-Learn MLPClassifier: Stochastic Learning Strategies
Linear Discriminant Analysis for Classification
Plot Kernel Ridge Regression
Plot Random Forest Regression Multioutput
Comparison Between Grid Search and Successive Halving
Plot Pca vs Fa Model Selection
Species Distribution Modeling
Data Scaling and Transformation
Demonstrating KBinsDiscretizer Strategies
FeatureHasher and DictVectorizer Comparison
Precompute Gram Matrix for ElasticNet
Plot Huber vs Ridge
Scikit-Learn Lasso Regression
Sparse Signal Recovery with Orthogonal Matching Pursuit
Plot SGD Separating Hyperplane
Step-by-Step Logistic Regression
Empirical Evaluation of K-Means Initialization
Neighborhood Components Analysis
Kernel Density Estimate of Species Distributions
Affinity Propagation Clustering
Hierarchical Clustering Dendrogram
Comparing BIRCH and MiniBatchKMeans
Bisecting K-Means and Regular K-Means Performance Comparison
Comparing Clustering Algorithms
Demo of HDBSCAN Clustering Algorithm
Mean-Shift Clustering Algorithm
Unsupervised Clustering with K-Means
Random Forest OOB Error Estimation
Pixel Importances with Parallel Forest of Trees
Image Segmentation with Hierarchical Clustering
Plot Dict Face Patches
Gaussian Processes on Discrete Data Structures
Spectral Clustering for Image Segmentation
SVM Tie Breaking
Plot GPR Co2
Boosted Decision Tree Regression
Bias-Variance Decomposition with Bagging
Scikit-Learn Elastic-Net Regression Model
Plot Agglomerative Clustering
Map Data to a Normal Distribution
Cross-Validation with Linear Models
SVM: Maximum Margin Separating Hyperplane
SVM for Unbalanced Classes
Preprocessing Techniques in Scikit-Learn
Agglomerative Clustering Metrics
Logistic Regression Classifier on Iris Dataset
Scikit-Learn Multi-Class SGD Classifier
Incremental Principal Component Analysis on Iris Dataset
Sparse Inverse Covariance Estimation
Nearest Centroid Classification
Probabilistic Predictions with Gaussian Process Classification
Gradient Boosting Monotonic Constraints
Scikit-Learn Confusion Matrix
Recognizing Hand-Written Digits
Gradient Boosting Regularization
Label Propagation Learning
Semi-Supervised Learning Algorithms
Nonlinear Data Regression Techniques
Prediction for Bitcoin Price
Shrinkage Covariance Estimation
Visualize High-Dimensional Data with MDS
Gaussian Mixture Model Covariances
Gaussian Mixture Model Selection
Semi-Supervised Classifiers on the Iris Dataset
Explicit Feature Map Approximation for RBF Kernels
Plot Pca vs Lda
Manifold Learning on Spherical Data
Faces Dataset Decompositions
Random Classification Dataset Plotting
Multilabel Dataset Generation with Scikit-Learn
Swiss Roll and Swiss-Hole Reduction
Scikit-Learn Libsvm GUI
Vector Quantization with KBinsDiscretizer
Hierarchical Clustering with Scikit-Learn
Transforming the Prediction Target
Feature Agglomeration for High-Dimensional Data
Feature Extraction with Scikit-Learn
Comparison of F-Test and Mutual Information
Curve Fitting with Bayesian Ridge Regression
Lasso and Elastic Net
Logistic Regression Model
Joint Feature Selection with Multi-Task Lasso
Applying Regularization Techniques with SGD
Theil-Sen Regression with Python Scikit-Learn
Compressive Sensing Image Reconstruction
Decision Tree Regression
Multi-Output Decision Tree Regression
Simple 1D Kernel Density Estimation
Local Outlier Factor for Novelty Detection
Outlier Detection with LOF
Density Estimation Using Kernel Density
Exploring K-Means Clustering with Python
Agglomerative Clustering on Digits Dataset
OPTICS Clustering Algorithm
Biclustering in Scikit-Learn
Regularization Path of L1-Logistic Regression
Support Vector Regression
Centroid Based Clustering
Neural Network Models
Gaussian Process Classification on Iris Dataset
Gaussian Process Classification
Gaussian Process Classification on XOR Dataset
Nonlinear Predictive Modeling Using Gaussian Process
Fit Gaussian Process Regression Model
Gaussian Process Regression: Kernels
Spectral Clustering and Other Clustering Methods
Nonlinear Pattern Recognition Techniques
Quickly Select Models with Cross Validation
Cross-Validation on Digits Dataset
Early Stopping of Gradient Boosting
Machine Learning Cross-Validation with Python
Linear Regression Example
Pairwise Metrics and Kernels in Scikit-Learn
Compare Cross Decomposition Methods
Nearest Neighbors Classification
SVM Classification Using Custom Kernel
SVM Classifier on Iris Dataset
Imputation of Missing Values
Decision Tree Classification with Python
Kernel Approximation Techniques in Scikit-Learn
Probabilistic Classification with Naive Bayes
Blind Source Separation
Independent Component Analysis with FastICA and PCA
Iris Flower Classification with Scikit-learn
Principal Components Analysis
Sparse Coding with Precomputed Dictionary
Wikipedia PageRank with Randomized SVD
Decomposing Signals in Components
Comparison of Covariance Estimators
Robust Covariance Estimation and Mahalanobis Distances Relevance
Robust Covariance Estimation in Python
Covariance Matrix Estimation with Scikit-Learn
Manifold Learning with Scikit-Learn
Discriminant Analysis Classification Algorithms
Plot Concentration Prior
Gaussian Mixture Models
Nonlinear Regression with Isotonic
Active Learning Withel Propagation
Bagging and Boosting Method
Hierarchical Clustering Exploration for Clustering
Guide of Tensorflow
Shallow Neural Network Implemented by Tensorflow 2
Tensorflow 2 Model Saving and Restoring
Train Handwritten Digits Recognition Neural Network
Calculation of Ridge Regression Coefficient
Linear Regression Fundamentals
Logistic Regression Classification with Scikit-Learn
Prediction for Beijing Housing Prices
Density Based Clustering
Image Compression Using Mini Batch K Means
Density-Based Clustering Application
K Nearest Neighbor Algorithm
Ridge Regression and Lasso Regression
Classification of Car Safety Evaluation Dataset
Perceptron and Artificial Neural Network
Teacher
Labby
Labby is the LabEx teacher.
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