Machine Learning Foundations: Neural Networks and Decision Trees

Moving beyond basic linear models is the key to unlocking the true power of modern artificial intelligence. Understanding how neural networks and decision trees process complex data allows you to build systems that can classify information, predict trends, and make smart decisions. This text-based course guides you from the fundamental concepts of non-linear models to building functional machine learning systems. You will learn how to structure data, select the right algorithms, and tune your models for maximum accuracy and generalization on unseen real-world data. What you'll learn: - Understand the foundational architecture of artificial neural networks and how they process information. - Build and train multi-class classification models using TensorFlow through clear, step-by-step code explanations. - Apply key machine learning development workflows, including bias-variance analysis and regularization, to prevent overfitting. - Construct decision trees and advanced tree ensemble methods like random forests and gradient boosted trees. - Implement modern model evaluation techniques to ensure your algorithms generalize reliably to new data. You will begin with core definitions and the underlying theory of neural pathways before moving into practical code implementations using TensorFlow. The curriculum then transitions to tree-based models, comparing different algorithmic approaches so you always know which tool to choose for your data. This course is designed for aspiring data professionals and developers who have a basic grasp of Python and want to dive into core machine learning algorithms. No advanced mathematical background is required to start. Start reading today to build a strong, practical foundation in modern machine learning.