1 Machine Learning Tutorials 🤖

Comprehensive guides covering machine learning algorithms, techniques, and applications.

1.1 Learning Path

1.1.1 Fundamentals

• Introduction to machine learning
• Types of learning (supervised, unsupervised, reinforcement)
• Model evaluation and validation
• Overfitting and underfitting

1.1.2 Supervised Learning

• Linear regression and polynomial features
• Logistic regression and classification
• Decision trees and random forests
• Support vector machines (SVM)
• Neural networks and deep learning

1.1.3 Unsupervised Learning

• K-means clustering and hierarchical clustering
• Principal component analysis (PCA)
• Dimensionality reduction techniques
• Anomaly detection

1.1.4 Advanced Topics

• Ensemble methods (bagging, boosting)
• Natural language processing (NLP)
• Computer vision and CNNs
• Reinforcement learning

1.2 Practical Projects

• Real-world datasets and implementations
• Code examples and notebooks
• Performance optimization tips
• Production deployment strategies

From theory to practice: Master machine learning with hands-on tutorials.