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

Ashok Kumar Yadav

ISBN 10: 3346792129 / ISBN 13: 9783346792129

Editore: GRIN Verlag, 2022

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Druck auf Anfrage Neuware - Printed after ordering - 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: The study material of Machine Learning is designed mainly for the students of engineering graduates. The syllabus is taken from Guru Gobind Singh Indraprastha University Delhi. It covers mainly all the topics of the subjects. The course is designed in a simple and precise manner. For complete knowledge and more study about the subject, students are advised to follow the books referred to in the references.Machine learning is a subfield of computer science, but is often also referred to as predictive analytics, or predictive modeling. Its goal and usage is to build new and/or leverage existing algorithms to learn from data, in order to build generalizable models that give accurate predictions, or to find patterns, particularly with new and unseen similar data.

Codice articolo 9783346792129

Dati bibliografici

• Titolo: Machine Learning. Supervised and unsupervised learning, latent semantic indexing, spectral clustering and Bellman equations
• Editore: GRIN Verlag
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 3346792129
• ISBN 13: 9783346792129
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 210 L x 148 A x 14 P (millimetri) 210 millimetri di larghezza per 148 millimetri di altezza per 14 millimetri di profondità
• Peso: 292 grammi

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Riassunto

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.