Scikit Learn Tutorial

Scikit-learn (also known as sklearn) is a widely-used open-source Python library for machine learning. It builds on other scientific libraries like NumPy, SciPy and Matplotlib to provide efficient tools for predictive data analysis and data mining.

It offers a consistent and simple interface for a range of supervised and unsupervised learning algorithms, including classification, regression, clustering, dimensionality reduction, model selection and preprocessing.

Why Learn Scikit-Learn?

• Wide Range of Algorithms: Scikit-learn provides access to a rich selection of algorithms for classification, regression, clustering and dimensionality reduction.
• Easy to Use and Understand: Clean API design and documentation make it suitable for both beginners and professionals.
• Interoperability: Works seamlessly with NumPy, Pandas, Matplotlib and other Python libraries.
• Feature Engineering and Evaluation Tools: Includes preprocessing utilities, pipelines and model evaluation metrics.
• Production-Ready: Optimized for performance and scalable to large datasets.

Installation and Setup

To set up Scikit-learn properly in your environment. Whether you're using Google Colab, Windows, Linux, or macOS, installation is straightforward using pip or conda. This section walks you through platform-specific setup steps.

• Install Sklearn in Colab
• Install Scikit-Learn in Windows
• Install Scikit-Learn on Linux
• Install Scikit-Learn on MacOS
• How To Upgrade Scikit-Learn Package In Anaconda

Scikit-Learn Basics

Understand the core components of Scikit-learn including datasets, preprocessing tools and model building. Learn how to use pipelines, transform data and identify important features for building efficient machine learning workflows.

• What is scikit-learn library
• Learning Model Building in Scikit-learn
• Top Inbuilt DataSets in Scikit-Learn
• Data Normalization with Scikit-Learn
• Feature Selection with Scikit-Learn
• Scikit-Learn for Data Preprocessing
• Identifying the Most Informative Features for scikit-learn Classifiers
• Sklearn Pipeline

Supervised Learning with Scikit-Learn

Supervised learning involves training models on labeled data to make predictions. Scikit-learn offers a variety of algorithms such as Linear Regression, SVM, Decision Trees and Random Forests to solve classification and regression problems.

• Classification Models in Scikit-Learn
• Linear Regression using sklearn
• Multiple Linear Regression With scikit-learn
• SVM and Kernel SVM with Scikit-Learn
• RBF SVM with Scikit Learn
• Decision Tree Classifiers with Scikit-Learn
• Decision Tree Regression using sklearn
• Random Forest Classifier using Scikit-learn
• KNN classifier using Scikit-Learn
• Gaussian Naive Bayes using Sklearn
• Stochastic Gradient Descent Regressor using Scikit-learn

Unsupervised Learning with Scikit-Learn

In unsupervised learning, models are trained on unlabeled data to find hidden patterns or groupings. Explore clustering techniques like K-Means and DBSCAN and dimensionality reduction methods like PCA and manifold learning.

• K-Means clustering using Scikit Learn
• DBSCAN algorithm using Sklearn
• PCA with scikit-learn
• Hierarchical Clustering with Scikit-Learn
• Gaussian Mixture Models (GMM) in Scikit Learn
• Manifold Learning methods in Scikit Learn

Model Evaluation with Scikit-Learn

Evaluating a machine learning model's performance is crucial to understanding its effectiveness. Scikit-learn provides tools for cross-validation, accuracy scoring, error metrics and visualization to fine-tune and validate your models.

• Cross-Validation Using K-Fold With Scikit-Learn
• score() and accuracy_score() methods in scikit-learn
• Euclidean Distance using Scikit-Learn
• Classification Metrics using Sklearn
• R² with Scikit-Learn
• Calculating RMSE Using Scikit-learn
• Clustering Performance Evaluation in Scikit Learn

Model Hyperparameter Tuning with Scikit-Learn

Fine-tuning model performance involves selecting the best hyperparameters. Scikit-learn offers tools like GridSearchCV and RandomizedSearchCV to automate this process, helping you strike the right balance between underfitting and overfitting.

• Grid Search and Randomized Search for Hyperparameter Estimation
• Model Hyper-parameters Tuning
• Validation Curve using Scikit-learn
• Bias-Variance Tradeoff
• Overfitting with Scikit-Learn

Projects with Scikit-Learn

Applying Scikit-learn to real-world projects solidifies your understanding of machine learning concepts. From classifying handwritten digits to clustering whisky profiles, these hands-on examples demonstrate how to build and evaluate models effectively.

• Recognizing Hand Written Digits in Scikit Learn
• Cancer cell classification using Scikit-learn
• Whisky Clustering with Scikit-learn
• Text Classification with scikit-learn