Fundamentals of Machine Learning and Predictive Analytics

Machine Learning and Predictive Analytics offers a basic and conceptual overview of intelligent algorithms and analytical techniques used to transform data into actionable insights. The book begins with the definition of machine learning, concept learning, and then most fundamental supervised learning technique i.e linear regression. Then it introduces some popular unsupervised techniques including k-means, Chameleon, DENCLUE and others. A brief discussion regarding Bayesian learning is also presented to make a better foundation for the readers. Some higher-level machine learning topics like dimensionality reduction and reinforcement learning also touched.
The text examines a wide range of machine learning algorithms, including linear and logistic regression, decision trees, support vector machines, naïve Bayes, k-nearest neighbors, clustering techniques and neural networks. This book provides a foundation so that the reader can understand today’s deep learning architectures, natural language processing, time-series forecasting, anomaly detection, recommendation systems, and reinforcement learning. It will help the readers to have a broad understanding of modern AI techniques.
A significant focus is placed on predictive analytics, covering model development, performance evaluation, cross-validation, hyperparameter optimization, and model deployment. Supported by algorithms, mathematical formulations, practical case studies, coding examples, visualizations, and real-world applications, this book serves as a valuable resource for students, researchers, educators, and professionals seeking to master machine learning techniques and predictive analytics for data-driven decision-making across diverse domains.