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.
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.