Simplified Machine Learning Dr. Pooja Sharma
(ebook)
(audiobook)
(audiobook)
- Autor:
- Dr. Pooja Sharma
- Wydawnictwo:
- BPB Publications
- Ocena:
- Stron:
- 266
- Dostępne formaty:
-
ePubMobi
Czytaj fragment
Opis
książki
:
Simplified Machine Learning
Explore the world of Artificial Intelligence with a deep understanding of Machine Learning concepts and algorithms
Key Features
A detailed study of mathematical concepts, Machine Learning concepts, and techniques.
Discusses methods for evaluating model performances and interpreting results.
Explores all types of Machine Learning (supervised, unsupervised, reinforcement, association rule mining, artificial neural network) in detail.
Comprises numerous review questions and programming exercises at the end of every chapter. Description
"Simplified Machine Learning" is a comprehensive guide that navigates readers through the intricate landscape of Machine Learning, offering a balanced blend of theory, algorithms, and practical applications.
The first section introduces foundational concepts such as supervised and unsupervised learning, regression, classification, clustering, and feature engineering, providing a solid base in Machine Learning theory. The second section explores algorithms like decision trees, support vector machines, and neural networks, explaining their functions, strengths, and limitations, with a special focus on deep learning, reinforcement learning, and ensemble methods. The book also covers essential topics like model evaluation, hyperparameter tuning, and model interpretability. The final section transitions from theory to practice, equipping readers with hands-on experience in deploying models, building scalable systems, and understanding ethical considerations.
By the end, readers will be able to leverage Machine Learning effectively in their respective fields, armed with practical skills and a strategic approach to problem-solving. What you will learn
Solid foundation in Machine Learning principles, algorithms, and methodologies.
Implementation of Machine Learning models using popular libraries like NumPy, Pandas, PyTorch, or scikit-learn.
Knowledge about selecting appropriate models, evaluating their performance, and tuning hyperparameters.
Techniques to pre-process and engineer features for Machine Learning models.
To frame real-world problems as Machine Learning tasks and apply appropriate techniques to solve them. Who this book is for
This book is designed for a diverse audience interested in Machine Learning, a core branch of Artificial Intelligence. Its intellectual coverage will benefit students, programmers, researchers, educators, AI enthusiasts, software engineers, and data scientists. Table of Contents
1. Introduction to Machine Learning
2. Data Pre-processing
3. Supervised Learning: Regression
4. Supervised Learning: Classification
5. Unsupervised Learning: Clustering
6. Dimensionality Reduction and Feature Selection
7. Association Rule Mining
8. Artificial Neural Network
9. Reinforcement Learning
10. Project
Appendix
Bibliography
A detailed study of mathematical concepts, Machine Learning concepts, and techniques.
Discusses methods for evaluating model performances and interpreting results.
Explores all types of Machine Learning (supervised, unsupervised, reinforcement, association rule mining, artificial neural network) in detail.
Comprises numerous review questions and programming exercises at the end of every chapter. Description
"Simplified Machine Learning" is a comprehensive guide that navigates readers through the intricate landscape of Machine Learning, offering a balanced blend of theory, algorithms, and practical applications.
The first section introduces foundational concepts such as supervised and unsupervised learning, regression, classification, clustering, and feature engineering, providing a solid base in Machine Learning theory. The second section explores algorithms like decision trees, support vector machines, and neural networks, explaining their functions, strengths, and limitations, with a special focus on deep learning, reinforcement learning, and ensemble methods. The book also covers essential topics like model evaluation, hyperparameter tuning, and model interpretability. The final section transitions from theory to practice, equipping readers with hands-on experience in deploying models, building scalable systems, and understanding ethical considerations.
By the end, readers will be able to leverage Machine Learning effectively in their respective fields, armed with practical skills and a strategic approach to problem-solving. What you will learn
Solid foundation in Machine Learning principles, algorithms, and methodologies.
Implementation of Machine Learning models using popular libraries like NumPy, Pandas, PyTorch, or scikit-learn.
Knowledge about selecting appropriate models, evaluating their performance, and tuning hyperparameters.
Techniques to pre-process and engineer features for Machine Learning models.
To frame real-world problems as Machine Learning tasks and apply appropriate techniques to solve them. Who this book is for
This book is designed for a diverse audience interested in Machine Learning, a core branch of Artificial Intelligence. Its intellectual coverage will benefit students, programmers, researchers, educators, AI enthusiasts, software engineers, and data scientists. Table of Contents
1. Introduction to Machine Learning
2. Data Pre-processing
3. Supervised Learning: Regression
4. Supervised Learning: Classification
5. Unsupervised Learning: Clustering
6. Dimensionality Reduction and Feature Selection
7. Association Rule Mining
8. Artificial Neural Network
9. Reinforcement Learning
10. Project
Appendix
Bibliography
Wybrane bestsellery
BPB Publications - inne książki
Dzięki opcji "Druk na żądanie" do sprzedaży wracają tytuły Grupy Helion, które cieszyły sie dużym zainteresowaniem, a których nakład został wyprzedany.
Dla naszych Czytelników wydrukowaliśmy dodatkową pulę egzemplarzy w technice druku cyfrowego.
Co powinieneś wiedzieć o usłudze "Druk na żądanie":
- usługa obejmuje tylko widoczną poniżej listę tytułów, którą na bieżąco aktualizujemy;
- cena książki może być wyższa od początkowej ceny detalicznej, co jest spowodowane kosztami druku cyfrowego (wyższymi niż koszty tradycyjnego druku offsetowego). Obowiązująca cena jest zawsze podawana na stronie WWW książki;
- zawartość książki wraz z dodatkami (płyta CD, DVD) odpowiada jej pierwotnemu wydaniu i jest w pełni komplementarna;
- usługa nie obejmuje książek w kolorze.
Masz pytanie o konkretny tytuł? Napisz do nas: sklep@helion.pl
Proszę wybrać ocenę!
Proszę wpisać opinię!
Książka drukowana
Proszę czekać...
Oceny i opinie klientów: Simplified Machine Learning Dr. Pooja Sharma (0) Weryfikacja opinii następuję na podstawie historii zamówień na koncie Użytkownika umieszczającego opinię. Użytkownik mógł otrzymać punkty za opublikowanie opinii uprawniające do uzyskania rabatu w ramach Programu Punktowego.