Book Description

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 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 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. 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