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