Dernière sortie
Machine Learning with Python: Foundations and Applications
About This BookTitle: Machine Learning with Python: Foundations and ApplicationsThis book, Machine Learning with Python: Foundations and Applications, is designed to offer a comprehensive introduction to machine learning using Python. The primary goal is to take readers from the fundamental concepts of machine learning to hands-on practical implementations using real-world examples. Python is the language of choice due to its extensive libraries, simplicity, and relevance in the data science community.
This book is divided into three main parts. The first volume focuses on understanding the basics of machine learning, key algorithms, and hands-on Python implementation. In this volume, we explore topics such as:The theoretical foundation of machine learningDifferent types of machine learning, including supervised, unsupervised, and reinforcement learningData preprocessing for machine learning tasksAn introduction to essential machine learning algorithmsEach chapter is carefully designed to build your knowledge step-by-step, ensuring that both beginners and those with some programming background can easily follow along.
This book is divided into three main parts. The first volume focuses on understanding the basics of machine learning, key algorithms, and hands-on Python implementation. In this volume, we explore topics such as:The theoretical foundation of machine learningDifferent types of machine learning, including supervised, unsupervised, and reinforcement learningData preprocessing for machine learning tasksAn introduction to essential machine learning algorithmsEach chapter is carefully designed to build your knowledge step-by-step, ensuring that both beginners and those with some programming background can easily follow along.
About This BookTitle: Machine Learning with Python: Foundations and ApplicationsThis book, Machine Learning with Python: Foundations and Applications, is designed to offer a comprehensive introduction to machine learning using Python. The primary goal is to take readers from the fundamental concepts of machine learning to hands-on practical implementations using real-world examples. Python is the language of choice due to its extensive libraries, simplicity, and relevance in the data science community.
This book is divided into three main parts. The first volume focuses on understanding the basics of machine learning, key algorithms, and hands-on Python implementation. In this volume, we explore topics such as:The theoretical foundation of machine learningDifferent types of machine learning, including supervised, unsupervised, and reinforcement learningData preprocessing for machine learning tasksAn introduction to essential machine learning algorithmsEach chapter is carefully designed to build your knowledge step-by-step, ensuring that both beginners and those with some programming background can easily follow along.
This book is divided into three main parts. The first volume focuses on understanding the basics of machine learning, key algorithms, and hands-on Python implementation. In this volume, we explore topics such as:The theoretical foundation of machine learningDifferent types of machine learning, including supervised, unsupervised, and reinforcement learningData preprocessing for machine learning tasksAn introduction to essential machine learning algorithmsEach chapter is carefully designed to build your knowledge step-by-step, ensuring that both beginners and those with some programming background can easily follow along.
