Scikit Learn Tutorial

Scikit-learn (sklearn) is a widely used open-source Python library for machine learning. Built on top of NumPy, SciPy and Matplotlib, it provides efficient and easy-to-use tools for predictive modeling and data analysis. Its consistent API design makes it suitable for both beginners and professionals.

• Supports supervised and unsupervised learning algorithms
• Provides preprocessing, feature engineering and pipeline tools
• Includes model evaluation and hyperparameter tuning utilities

Why Learn Scikit-Learn?

• Wide range of algorithms for classification, regression and clustering
• Clean and consistent API design
• Seamless integration with NumPy, Pandas and Matplotlib
• Built-in preprocessing and model evaluation tools
• Optimized for performance and scalability

Installation and Setup

Scikit-learn can be installed easily using pip or conda across platforms.

• Install in Google Colab
• Install on Windows
• Install on Linux
• Install on MacOS
• Upgrade using Anaconda

Scikit-Learn Basics

This section introduces the core components required to build machine learning models.

• Introduction
• Model Building Workflow
• Built-in DataSets
• Data Normalization
• Data Preprocessing
• Feature Selection
• Pipelines for structured workflows

Supervised Learning with Scikit-Learn

Supervised learning involves training models on labeled data to make predictions.

Classification Models

• Introduction
• SVM and Kernel SVM
• RBF SVM
• Decision Tree
• Random Forest
• KNN classifier
• Gaussian Naive Bayes

Regression Models

• Linear Regression
• Multiple Linear Regression
• Decision Tree Regression
  
• Stochastic Gradient Descent Regressor

Unsupervised Learning with Scikit-Learn

Unsupervised learning finds patterns in unlabeled data

• K-Means clustering
• DBSCAN algorithm
• Principal Component Analysis (PCA)
• Hierarchical Clustering
• Gaussian Mixture Models (GMM)
• Manifold Learning methods

Model Evaluation

Evaluating model performance ensures reliability and generalization.

• Cross-Validation
• Accuracy and scoring metrics
• Euclidean Distance
• Classification Metrics
• R² with Scikit-Learn
• RMSE calculation
• Clustering Evaluation Metrics

Hyperparameter Tuning

Optimizing model performance requires selecting the best hyperparameters.

• Introduction
• Grid Search and Randomized Search
• Validation Curve
• RandomForestClassifier & ExtraTreesClassifier
• Overfitting

Projects with Scikit-Learn

Practical projects help reinforce machine learning concepts.

• Hand digit recognition
• Cancer cell classification
• Whisky Clustering Analysis
• Text Classification

Related Articles

• Machine Learning
• Deep Learning