Advanced Machine Learning : Fundamentals and algorithms

Enregistré dans:
Détails bibliographiques
Auteur principal: Kumar Tyagi, Amit (19..-....). (Auteur)
Autres auteurs: Tripathi, Khushboo. (Auteur), Kumar Sharma, Avinash.
Support: E-Book
Langue: Anglais
Publié: New Delhi : BPB Publications.
Autres localisations: Voir dans le Sudoc
Résumé: Our book explains learning algorithms related to real-world problems, with implementations in languages like R, Python, etc. Key Features: Basic understanding of machine learning algorithms via MATLAB, R, and Python ; Inclusion of examples related to real-world problems, case studies, and questions related to futuristic technologies ; Adding futuristic technologies related to machine learning and deep learning.Description: Our book is divided into several useful concepts and techniques of machine learning. This book serves as a valuable resource for individuals seeking to deepen their understanding of advanced topics in this field.Learn about various learning algorithms, including supervised, unsupervised, and reinforcement learning, and their mathematical foundations. Discover the significance of feature engineering and selection for enhancing model performance. Understand model evaluation metrics like accuracy, precision, recall, and F1-score, along with techniques like cross-validation and grid search for model selection. Explore ensemble learning methods along with deep learning, unsupervised learning, time series analysis, and reinforcement learning techniques. Lastly, uncover real-world applications of the machine and deep learning algorithms.After reading this book, readers will gain a comprehensive understanding of machine learning fundamentals and advanced techniques. With this knowledge, readers will be equipped to tackle real-world problems, make informed decisions, and develop innovative solutions using machine and deep learning algorithms. What you will learn: Ability to tackle complex machine learning problems ; Understanding of foundations, algorithms, ethical issues, and how to implement each learning algorithm for their own use/ with their data ; Efficient data analysis for real-time data will be understood by researchers/ students ; Using data analysis in near future topics and cutting-edge technologies. Who this book is for: This book is ideal for students, professors, and researchers. It equips industry experts and academics with the technical know-how and practical implementations of machine learning algorithms
Accès en ligne: Accès à l'E-book
LEADER 04292nmm a2200433 i 4500
001 ebook-280851189
005 20241014161306.0
007 cu|uuu---uuuuu
008 241014s2024||||ii ||||g|||| ||||||eng d
035 |a FRCYB88958022 
035 |a FRCYB26088958022 
035 |a FRCYB24888958022 
035 |a FRCYB29388958022 
035 |a FRCYB084688958022 
035 |a FRCYB087588958022 
035 |a FRCYB56788958022 
035 |a FRCYB097088958022 
035 |a FRCYB087088958022 
040 |a ABES  |b fre  |e AFNOR 
041 0 |a eng  |2 639-2 
100 1 |0 (IdRef)258346574  |1 http://www.idref.fr/258346574/id  |a Kumar Tyagi, Amit  |d (19..-....).  |4 aut.  |e Auteur 
245 1 0 |a Advanced Machine Learning :  |b Fundamentals and algorithms   |c Dr. Amit Kumar Tyagi, Dr. Khushboo Tripathi, Dr. Avinash Kumar Sharma. 
264 1 |a New Delhi :  |b BPB Publications. 
264 2 |a Paris :  |b Cyberlibris,  |c 2024. 
336 |b txt  |2 rdacontent 
337 |b c  |2 rdamedia 
337 |b b  |2 isbdmedia 
338 |b ceb  |2 RDAfrCarrier 
500 |a Couverture (https://static2.cyberlibris.com/books_upload/136pix/9789355516343.jpg). 
506 |a L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement  |e Cyberlibris 
520 |a Our book explains learning algorithms related to real-world problems, with implementations in languages like R, Python, etc. Key Features: Basic understanding of machine learning algorithms via MATLAB, R, and Python ; Inclusion of examples related to real-world problems, case studies, and questions related to futuristic technologies ; Adding futuristic technologies related to machine learning and deep learning.Description: Our book is divided into several useful concepts and techniques of machine learning. This book serves as a valuable resource for individuals seeking to deepen their understanding of advanced topics in this field.Learn about various learning algorithms, including supervised, unsupervised, and reinforcement learning, and their mathematical foundations. Discover the significance of feature engineering and selection for enhancing model performance. Understand model evaluation metrics like accuracy, precision, recall, and F1-score, along with techniques like cross-validation and grid search for model selection. Explore ensemble learning methods along with deep learning, unsupervised learning, time series analysis, and reinforcement learning techniques. Lastly, uncover real-world applications of the machine and deep learning algorithms.After reading this book, readers will gain a comprehensive understanding of machine learning fundamentals and advanced techniques. With this knowledge, readers will be equipped to tackle real-world problems, make informed decisions, and develop innovative solutions using machine and deep learning algorithms. What you will learn: Ability to tackle complex machine learning problems ; Understanding of foundations, algorithms, ethical issues, and how to implement each learning algorithm for their own use/ with their data ; Efficient data analysis for real-time data will be understood by researchers/ students ; Using data analysis in near future topics and cutting-edge technologies. Who this book is for: This book is ideal for students, professors, and researchers. It equips industry experts and academics with the technical know-how and practical implementations of machine learning algorithms 
559 2 |b 1. Introduction to Machine Learning  |b 2. Statistical Analysis  |b 3. Linear Regression  |b 4. Logistic Regression  |b 5. Decision Trees  |b 6. Random Forest  |b 7. Rule-Based Classifiers  |b 8. Naïve Bayesian Classifier  |b 9. K-Nearest Neighbors Classifiers  |b 10. Support Vector Machine  |b 11. K-Means Clustering  |b 12. Dimensionality Reduction  |b 13. Association Rules Mining and FP Growth  |b 14. Reinforcement Learning  |b 15. Applications of ML Algorithms  |b 16. Applications of Deep Learning  |b 17. Advance Topics and Future Directions 
700 1 |a Tripathi, Khushboo.  |4 aut.  |e Auteur 
700 1 |a Kumar Sharma, Avinash.  |4 aut.  |e Auteur 
856 |q HTML  |u https://srvext.uco.fr/login?url=https://univ.scholarvox.com/book/88958022  |w Données éditeur  |z Accès à l'E-book 
886 2 |2 unimarc  |a 181  |a i#  |b xxxe## 
993 |a E-Book  
994 |a BNUM 
995 |a 280851189