A practical guide to mastering Classification algorithms for Machine learning 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. The book starts with an introduction to problem-solving in machine learning and subsequently focuses on classification problems. It then explores the Naive Bayes algorithm, a probabilistic method widely used in industrial applications. The application of Bayes Theorem and underlying assumptions in developing the Naive 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. What you will learn â— Learn how to apply Naive Bayes algorithm to solve real-world classification problems. â— Explore the concept of K-Nearest Neighbor algorithm for classification tasks. â— Dive into the Logistic Regression algorithm for classification. â— Explore techniques like Bagging and Random Forest to overcome the weaknesses of Decision Trees. â— 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.
