Posts tagged with "machine-learning".
Explore core concepts of unsupervised learning, including K-means clustering, optimization strategies, and how anomaly detection systems are designed and evaluated.
A complete guide to decision trees, covering entropy, information gain, one-hot encoding, regression trees, and ensemble methods like Random Forest and XGBoost.
Learn how to make decisions, evaluate models, handle bias and variance, and manage real-world ML workflows with cross-validation, error analysis, and transfer learning.
Explore how neural networks are trained with gradient descent, softmax, and backpropagation using TensorFlow. Understand activation functions and multiclass classification techniques.
A comprehensive guide to neural networks, forward propagation, TensorFlow implementation, and efficient matrix computations.
A comprehensive breakdown of logistic regression, sigmoid function, loss functions, and regularization for classification tasks.