Miller Training Systems Using Python Statistical Modeling

Explore popular techniques for modeling your data in Python
1. Auflage 2024
ISBN: 978-1-83882-064-0
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 290 Seiten

Leverage the power of Python and statistical modeling techniques for building accurate predictive modelsKey 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 DescriptionPython'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 forIf 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.

Autoren/Hrsg.

Miller, Curtis

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur

Inhaltsverzeichnis

Table of Contents - Classical Statistical Analysis
- Introduction to Supervised Learning
- Binary Prediction Models
- Regression Analysis and How to Use It
- Neural Networks
- Clustering Techniques
- Dimensionality Reduction

Über Autor(innen)

Miller Curtis :

Curtis Miller is a doctoral candidate at the University of Utah studying mathematical statistics. He writes software for both research and personal interest, including the R package (CPAT) available on the Comprehensive R Archive Network (CRAN). Among Curtis Miller's publications are academic papers along with books and video courses all published by Packt Publishing. Curtis Miller's video courses include Unpacking NumPy and Pandas, Data Acquisition and Manipulation with Python, Training Your Systems with Python Statistical Modelling, and Applications of Statistical Learning with Python. His books include Hands-On Data Analysis with NumPy and Pandas.