Neural networks are flexible models that can handle various data types. Accuracy is crucial for assessing model performance. Training and testing sets are essential for model evaluation
Data preprocessing transforms raw text into machine learning-understandable format. Twitter Customer Support dataset used for demonstration. Bag of words created to store word frequencies without order
Python list is an ordered, zero-indexed collection of objects. Duplicate removal is important for data preprocessing
Elbow method determines cluster number by plotting explained variation vs clusters. Method was proposed by Robert L. Thorndike in 1953. Used to choose number of parameters in other data-driven models
KNN is a supervised learning algorithm used for both classification and regression. It is a lazy learning algorithm that saves all training data during training. KNN is a non-parametric algorithm that doesn't assume data characteristics
Small object detection is one of the most challenging computer vision problems. Recent models like EfficientDet show only 12% mAP for small objects. Small objects are most likely to have data labeling errors