Training Systems Using Python Statistical Modeling

Book Description

Leverage the power of Python and statistical modeling techniques for building accurate predictive models

Key Features

• Get introduced to Python’s rich suite of libraries for statistical modeling
• Implement regression, clustering and train neural networks from scratch
• Includes 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 led it to become the choice of tool for many data scientists and machine learning developers today. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book takes you through an exciting journey, of using these libraries to implement effective statistical models for predictive analytics.

You’ll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will look at supervised learning, where you will explore the principles of machine learning and train different machine learning models from scratch. You will also work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. This book also covers algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. You will also learn how neural networks can be trained and deployed for more accurate predictions, and which Python libraries can be used to implement them.

By the end of this book, you will have all 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 various Python packages for statistical analysis
• Implement algorithms such as Naive Bayes, random forests, and more
• 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.

Table of Contents

1. Classical Statistical Analysis
2. Introduction to Supervised Learning
3. Binary Prediction Models
4. Regression Analysis and How to Use It
5. Neural Networks
6. Clustering Techniques
7. Dimensionality Reduction

中文：

书名：Training Systems Using Python Statistical Modeling

利用Python的强大功能和统计建模技术构建准确的预测模型

Key Features

• 了解Python丰富的统计建模库套件
• 从头开始实施回归、聚类和训练神经网络
• 包括使用Python语言培训端到端机器学习系统的真实示例

Book Description

Python的易用性和多功能性使其成为当今许多数据科学家和机器学习开发人员的首选工具。其丰富的库被广泛用于数据分析，更重要的是，用于构建最先进的预测模型。这本书带你经历了一段激动人心的旅程，使用这些库来实现预测分析的有效统计模型。

您将从学习经典统计分析开始，在那里您将学习如何使用熊猫来计算描述性统计。您将了解监督学习，在其中您将探索机器学习的原理，并从头开始训练不同的机器学习模型。您还将使用二进制预测模型，例如使用k近邻、决策树和随机森林进行数据分类。本书还介绍了回归分析的算法，如岭回归和套索回归，以及它们在Python语言中的实现。您还将学习如何训练和部署神经网络以实现更准确的预测，以及可以使用哪些Python库来实现这些预测。

在本书结束时，您将掌握设计、构建和部署用于机器学习的企业级统计模型所需的所有知识，包括使用Python及其丰富的预测分析库生态系统。

What you will learn

• 了解统计建模的重要性
• 了解用于统计分析的各种Python包
• Implement algorithms such as Naive Bayes, random forests, and more
• 使用PythonSCRIPKIT的学习库从头开始构建预测模型
• 实现回归分析和聚类
• 了解如何使用Python语言训练神经网络

这本书是为谁而写的

如果你是一名数据科学家、统计学家或机器学习开发人员，希望使用流行的统计技术培训和部署有效的机器学习模型，那么这本书是为你而写的。要从这本书中获得最大的收获，就需要掌握有关Python编程的知识。

Table of Contents

1. 经典统计分析
2. Introduction to Supervised Learning
3. 二元预测模型
4. 回归分析及其应用
5. Neural Networks
6. 集群技术
7. 降维