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Sklearn Practice Labs (Deprecated)

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.

PythonData Science

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

Introduction

This Sklearn Practice Labs course is designed to help you master the practical application of the popular machine learning library, Scikit-learn (Sklearn). Through a series of carefully curated labs, you will have the opportunity to apply your Sklearn knowledge to real-world projects, honing your coding skills and learning to write clean, efficient code.

🎯 Tasks

In this Course, you will learn:

  • How to implement a wide range of Sklearn algorithms, including classification, regression, clustering, and dimensionality reduction techniques
  • How to preprocess and prepare data for Sklearn models
  • How to tune model hyperparameters and evaluate model performance
  • How to apply Sklearn to solve practical problems in areas such as image recognition, natural language processing, and predictive analytics

🏆 Achievements

After completing this Course, you will be able to:

  • Confidently apply Sklearn to tackle a variety of machine learning problems
  • Develop a deep understanding of Sklearn's core functionalities and best practices
  • Improve your coding skills by working through well-designed, hands-on Sklearn projects
  • Become proficient in writing clean, efficient, and maintainable Sklearn-based code

Teacher

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Labby
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Sklearn Practice Labs (Deprecated)
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