Training Systems Using Python Statistical Modeling

• Verfasst von: Miller, Curtis [VerfasserIn]
• Titel: Training Systems Using Python Statistical Modeling
• Institutionen: Safari, an O’Reilly Media Company.
• Verf.angabe: Miller, Curtis
• Ausgabe: 1st edition
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: Packt Publishing
• Jahr: 2019
• Umfang: 1 online resource (290 pages)
• Fussnoten: Online resource; Title from title page (viewed May 20, 2019)
• ComputerInfo: Mode of access: World Wide Web.
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Electronic books ; local
• K10plus-PPN: 1694856143
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m3624783769
• Lokale URL Inst.: Zum Volltext

Abstract

Leverage the power of Python and statistical modeling techniques for building accurate predictive models Key Features Get started with Python's rich suite of libraries for statistical modeling Implement regression and clustering, and train neural networks from scratch Discover real-world examples on training end-to-end machine learning systems in Python Book Description Python's ease-of-use and multi-purpose nature has made it one of the most popular tools for data scientists and machine learning developers. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book is designed to guide you through using these libraries to implement effective statistical models for predictive analytics. You'll start by delving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will focus on supervised learning, which will help you explore the principles of machine learning and train different machine learning models from scratch. Next, you will work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. The book will also cover algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. In later chapters, you will learn how neural networks can be trained and deployed for more accurate predictions, and understand which Python libraries can be used to implement them. By the end of this book, you will have the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics. What you will learn Understand the importance of statistical modeling Learn about the different Python packages for statistical analysis Implement algorithms such as Naive Bayes and random forests Build predictive models from scratch using Python's scikit-learn library Implement regression analysis and clustering Learn how to train a neural network in Python Who this book is for If you are a data scientist, a statistician or a machine learning developer looking to train and deploy effective machine learning models using popular statistical techniques, then this book is for you. Knowledge of Python programming is required to get the most out of this book. Downloading the example code for this ebook: You can download the example code fil...