GRIN Verlag Machine Learning. Supervised and unsupervised learning, latent semantic indexing, spectral clustering and Bellman equations

Document from the year 2022 in the subject Computer Sciences - Artificial Intelligence, grade: B.Tech, Amity University (Amity School of Engineering and Technology), language: English, abstract: Unlock the secrets of intelligent systems and embark on a journey into the fascinating world of machine learning! This comprehensive guide provides a robust foundation in the core principles and practical applications of this transformative field. Delve into the fundamental concepts of learning systems, exploring the goals and diverse applications of machine learning across various industries. Master the art of preparing data for success, from selection and preprocessing to transformation techniques, and understand the critical role of training, test, and validation datasets in building robust models. Unravel the complexities of supervised and unsupervised learning, gaining insights into various algorithms and the unique challenges associated with each approach. Discover how to combat overfitting and ensure your models generalize effectively to new, unseen data. Explore a rich landscape of classification families, including linear and non-linear discriminative models, decision trees, conditional models like linear and logistic regression, generative models, and nearest neighbor algorithms. Sharpen your skills with an in-depth examination of logistic regression, mastering its function, representation, probability prediction, model learning processes, and data preparation requirements. Uncover the inner workings of the perceptron model and its learning algorithm. Finally, journey into the realm of statistical distributions with a focus on the exponential family, including normal, Poisson, exponential, Bernoulli, and binomial distributions, providing a crucial context for the probabilistic nature of many machine learning algorithms. Whether you're a student, a researcher, or a seasoned professional, this book equips you with the knowledge and skills to harness the power of machine learning and create intelligent solutions for a data-driven world. Explore essential topics such as data preprocessing, algorithm selection, model evaluation, and strategies for preventing overfitting, ensuring you build models that are both accurate and reliable. Learn how to leverage techniques like logistic regression and understand the nuances of perceptron models, providing you with practical tools for real-world applications. Dive deep into the mathematical foundations with a focus on the exponential family of distributions, solidifying your understanding of the statistical underpinnings of machine learning.