Scikit-learn Tutorials 📚

Welcome to the Scikit-learn tutorial section! This is your gateway to mastering machine learning with Python. Below are key topics and resources to help you get started.

📘 Core Concepts

• Supervised Learning 📊
  • Linear Regression 📈
  • Classification Algorithms 🧩 (e.g., SVM, Random Forest)
  • Model Evaluation Metrics 📊 (Accuracy, Precision, Recall)
• Unsupervised Learning 🌀
  • Clustering Techniques 🧬 (K-Means, DBSCAN)
  • Dimensionality Reduction 📏 (PCA, t-SNE)
  • Anomaly Detection 🔍

🛠️ Hands-On Tutorials

1. Beginner's Guide to Scikit-learn
   • Install and configure the library 📦
   • Load datasets 📁 (e.g., Iris, MNIST)
2. Advanced Topics
   • Hyperparameter tuning 🎯 (Grid Search, Random Search)
   • Ensemble methods 🧑‍🤝‍🧑 (Bagging, Boosting)
   • Custom pipeline creation 🧱

📈 Visual Learning

*Understanding the fundamentals of ML* *Visualizing how decision trees split data*

🌐 Expand Your Knowledge

• Official Scikit-learn Documentation for in-depth guides
• Python for Data Science to strengthen foundational skills
• ML Algorithms Comparison for visual insights

Explore these resources to build your machine learning expertise! 🚀