/Machine-Learning-Basics---Python

A compilation of illustrative examples for some classical machine learning techniques

Primary LanguageJupyter Notebook

Machine Learning Basics in Python 🐍

A compilation of illustrative examples for some classical machine learning techniques and algorithms. Perfect for beginners and practitioners looking to refresh their knowledge. 📊🤖

Supervised Learning 🎓

Regression 📈

  • Regression Algorithms:
    • Linear Regression
    • Polynomial Regression
    • Lasso Regression
    • Ridge Regression
    • Support Vector Regression (SVR)

Classification 🏷️

  • Decision Trees and Random Forest
  • K-Nearest Neighbors (KNN) and Logistic Regression
  • Naive Bayes
  • Support Vector Classification (SVC)

Unsupervised Learning 🧩

Clustering 📊

  • K-means Clustering
  • Principal Component Analysis (PCA)

Recommended Systems 🎯

  • Item-Based Collaborative Filtering

Cross Validation ✅

  • K-fold Cross Validation

One-Hot Encoding 🔢

  • Transforming categorical data into a binary matrix representation for machine learning algorithms.