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A practical guide to mastering Classification algorithms for Machine learningKey Features● Get familiar with all the state-of-the-art classification algorithms for machine learning.● Understand the mathematical foundations behind building machine learning models.● Learn how to apply machine learning models to solve real-world industry problems.DescriptionClassification algorithms are essential in machine learning as they allow us to make predictions about the class or category of an input by considering its features. These algorithms have a significant impact on multiple applications like spam filtering, sentiment analysis, image recognition, and fraud detection. If you want to expand your knowledge about classification algorithms, this book is the ideal resource for you.The book starts with an introduction to problem-solving in machine learning and subsequently focuses on classification problems. It then explores the Naïve Bayes algorithm, a probabilistic method widely used in industrial applications. The application of Bayes Theorem and underlying assumptions in developing the Naïve Bayes algorithm for classification is also covered. Moving forward, the book centers its attention on the Logistic Regression algorithm, exploring the sigmoid function and its significance in binary classification. The book also covers Decision Trees and discusses the Gini Factor, Entropy, and their use in splitting trees and generating decision leaves. The Random Forest algorithm is also thoroughly explained as a cutting-edge method for classification (and regression). The book concludes by exploring practical applications such as Spam Detection, Customer Segmentation, Disease Classification, Malware Detection in JPEG and ELF Files, Emotion Analysis from Speech, and Image Classification.By the end of the book, you will become proficient in utilizing classification algorithms for solving complex machine learning problems.What you will learn● Learn how to apply Naïve Bayes algorithm to solve real-world classification problems.● Explore the concept of K-Nearest Neighbor algorithm for classification tasks.● Dive into the Logistic Regression algorithm for classification.● Explore techniques like Bagging and Random Forest to overcome the weaknesses of Decision Trees.● Learn how to combine multiple models to improve classification accuracy and robustness.Who this book is forThis book is for Machine Learning Engineers, Data Scientists, Data Science Enthusiasts, Researchers, Computer Programmers, and Students who are interested in exploring a wide range of algorithms utilized for classification tasks in machine learning.Table of Contents1. Introduction to Machine Learning2. Naïve Bayes Algorithm3. K-Nearest Neighbor Algorithm4. Logistic Regression5. Decision Tree Algorithm6. Ensemble Models7. Random Forest Algorithm8. Boosting AlgorithmAnnexure 1: Jupyter NotebookAnnexure 2: PythonAnnexure 3: Singular Value DecompositionAnnexure 4: Preprocessing Textual DataAnnexure 5: Stemming and LamentationAnnexure 6: VectorizersAnnexure 7: EncodersAnnexure 8: Entropy A practical guide to mastering Classification algorithms for Machine learning Key Features Get familiar with all the state-of-the-art classification algorithms for machine learning. Understand the mathematical foundations behind building machine learning models. Learn how to apply machine learning models to solve real-world industry problems. Description Classification algorithms are essential in machine learning as they allow us to make predictions about the class or category of an input by considering its features. These algorithms have a significant impact on multiple applications like spam filtering, sentiment analysis, image recognition, and fraud detection. If you want to expand your knowledge about classification algorithms, this book is the ideal resource for you. The book starts with an introduction to problem-solving in machine learning and subsequently focuses on classification problems. It then explores the Nave Bayes algorithm, a probabilistic method widely used in industrial applications. The application of Bayes Theorem and underlying assumptions in developing the Nave Bayes algorithm for classification is also covered. Moving forward, the book centers its attention on the Logistic Regression algorithm, exploring the sigmoid function and its significance in binary classification. The book also covers Decision Trees and discusses the Gini Factor, Entropy, and their use in splitting trees and generating decision leaves. The Random Forest algorithm is also thoroughly explained as a cutting-edge method for classification (and regression). The book concludes by exploring practical applications such as Spam Detection, Customer Segmentation, Disease Classification, Malware Detection in JPEG and ELF Files, Emotion Analysis from Speech, and Image Classification. By the end of the book, you will become proficient in utilizing classification algorithms for solving complex machine learning problems. What you will learn Learn how to apply Nave Bayes algorithm to solve real-world classification problems. Explore the concept of K-Nearest Neighbor algorithm for classification tasks. Dive into the Logistic Regression algorithm for classification. Explore techniques like Bagging and Random Forest to overcome the weaknesses of Decision Trees. Learn how to combine multiple models to improve classification accuracy and robustness. Who this book is for This book is for Machine Learning Engineers, Data Scientists, Data Science Enthusiasts, Researchers, Computer Programmers, and Students who are interested in exploring a wide range of algorithms utilized for classification tasks in machine learning. Table of Contents 1. Introduction to Machine Learning 2. Nave 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 Classification algorithms are essential in machine learning as they allow us to make predictions about the class or category of an input by considering its features. These algorithms have a significant impact on multiple applications like spam filtering, sentiment analysis, image recognition, and fraud detection. If you want to expand your knowledge about classification algorithms, this book is the ideal resource for you. The book starts with an introduction to problem-solving in machine learning and subsequently focuses on classification problems. It then explores the Naïve Bayes algorithm, a probabilistic method widely used in industrial applications. The application of Bayes Theorem and underlying assumptions in developing the Naïve Bayes algorithm for classification is also covered. Moving forward, the book centers its attention on the Logistic Regression algorithm, exploring the sigmoid function and its significance in binary classification. The book also covers Decision Trees and discusses the Gini Factor, Entropy, and their use in splitting trees and generating decision leaves. The Random Forest algorithm is also thoroughly explained as a cutting-edge method for classification (and regression). The book concludes by exploring practical applications such as Spam Detection, Customer Segmentation, Disease Classification, Malware Detection in JPEG and ELF Files, Emotion Analysis from Speech, and Image Classification. By the end of the book, you will become proficient in utilizing classification algorithms for solving complex machine learning problems. 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