Mastering Classification Algorithms for Machine Learning: Learn how to apply Classification algorithms for effective Machine Learning solutions

Description

Recent combinations of semantic technology and artificial intelligence (AI) present new techniques to build intelligent systems that identify more precise results. Semantic AI in Knowledge Graphs locates itself at the forefront of this novel development, uncovering the role of machine learning to extend the knowledge graphs by graph mapping or corpus-based ontology learning. Securing efficient results via the combination of symbolic AI and statistical AI such as entity extraction based on machine learning, text mining methods, semantic knowledge graphs, and related reasoning power, this book is the first of its kind to explore semantic AI and knowledge graphs. A range of topics are covered, from neuro-symbolic AI, explainable AI and deep learning to knowledge discovery and mining, and knowledge representation and reasoning. A trailblazing exploration of semantic AI in knowledge graphs, this book is a significant contribution to both researchers in the field of AI and data mining as well as beginner academicians. 1. Introduction to Machine Learning 2. Naïve Bayes Algorithm 3. K-Nearest Neighbor Algorithm 4. Logistic Regression 5. Decision Tree Algorithm 6. Ensemble Models 7. Random Forest Algorithm 8. Boosting Algorithm Annexure 1: Jupyter Notebook Annexure 2: Python Annexure 3: Singular Value Decomposition Annexure 4: Preprocessing Textual Data Annexure 5: Stemming and Lamentation Annexure 6: Vectorizers Annexure 7: Encoders Annexure 8: Entropy