Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Machine Learning. Supervised and unsupervised learning, latent semantic indexing, spectral clustering and Bellman equations

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

Yadav, Ashok Kumar

Paperback

Networking

ISBN10: 3346792129
ISBN13: 9783346792129
Publisher: Grin Verlag
Published: Dec 28 2022
Pages: 196
Weight: 0.58
Height: 0.45 Width: 5.83 Depth: 8.27
Language: English
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 mach

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