Classification Models: Supervised Machine Learning in Python

Describe the input and output of a classification model Prepare data with feature engineering techniques Tackle both binary and multiclass classification problems Implement Support Vector Machines, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, Neural Networks, logistic regression models on Python Use a variety of performance metrics such as confusion matrix, accuracy, precision, recall, ROC curve and AUC score. Use a variety of performance metrics such as confusion matrix, accuracy, precision, recall, ROC curve and AUC score. Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There's an endless supply of industries and applications that machine learning can make more efficient and intelligent. Supervised machine learning is the underlying method behind a large part of this.