Simple Machine Learning Code Tutorial for Beginners with Sklearn

This beginner-friendly tutorial walks you through practical machine learning using Scikit-Learn (Sklearn), one of the most accessible Python libraries for AI development. Learn to build a complete machine learning workflow in just 24 minutes, covering essential steps from environment setup to model deployment. Follow along as you install Sklearn, load and explore the California Housing dataset, split data properly, train models using Linear Regression, Random Forest, and Gradient Boosting algorithms, and optimize performance with Polynomial Features and Hyperparameter Tuning. Master model evaluation using R² scores and learn how to save and load models with Joblib. The tutorial breaks down complex concepts into simple, logical steps with clear explanations suitable for those new to data science or looking to enhance their practical skills. Additional learning resources are provided to help deepen your understanding of machine learning fundamentals.