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.
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.
WHAT YOU WILL LEARN
● Solid foundation in Machine Learning principles, algorithms, and methodologies.
● Implementation of Mach... See more
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.
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.
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.
