Email Spam Detection Using Naive Bayes And Random Forest Classifiers

The Complet Decision Tree & Random Forest Course with Python-Mastering Decision Tree & Random Forest using real data

eXtreme Gradient Boosting, K Nearest Neighbour, Naïve Bayes, Random Forest for Prediction Geo-Hazards and Air pollution

​This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed.

Applications: decrypting ciphers, spam detection, sentiment analysis, article spinners, and latent semantic analysis.

GaussianNB, MultinomialNB, BernoulliNB, DictVectorizer, LogisticRegression

From Scratch (Data Collection to Apply Algorithms) learn to Develop Naïve Bayes | Random Forest | SVM and Much More

Learn to make and understand predictions with decision trees and random forests. Includes detailed Python demos.

Master a crucial artificial intelligence algorithm and skyrocket your Python programming skills