Mastering Predictive Analytics with scikit-learn and TensorFlow: Implement machine learning techniques to build advanced predictive models using Python

Alan Fontaine

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商品描述

Learn advanced techniques to improve the performance and quality of your predictive models

Key Features

  • Use ensemble methods to improve the performance of predictive analytics models
  • Implement feature selection, dimensionality reduction, and cross-validation techniques
  • Develop neural network models and master the basics of deep learning

Book Description

Python is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems.

This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics.

By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis.

What you will learn

  • Use ensemble algorithms to obtain accurate predictions
  • Apply dimensionality reduction techniques to combine features and build better models
  • Choose the optimal hyperparameters using cross-validation
  • Implement different techniques to solve current challenges in the predictive analytics domain
  • Understand various elements of deep neural network (DNN) models
  • Implement neural networks to solve both classification and regression problems

Who this book is for

Mastering Predictive Analytics with scikit-learn and TensorFlow is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to progress from basic predictive models to building advanced models and producing more accurate predictions. Prior knowledge of Python and familiarity with predictive analytics concepts are assumed.

Table of Contents

  1. Ensemble Methods for Regression and Classification
  2. Cross-validation and Parameter Tuning
  3. Working with Features
  4. Introduction to Artificial Neural Networks and TensorFlow
  5. Predictive Analytics with TensorFlow and Deep Neural Networks

商品描述(中文翻譯)

學習提升預測模型性能和質量的高級技巧

主要特點:
- 使用集成方法提升預測分析模型的性能
- 實施特徵選擇、降維和交叉驗證技術
- 開發神經網絡模型並掌握深度學習的基礎知識

書籍描述:
Python是一種提供多種功能的程式語言,可應用於數據科學領域。《使用scikit-learn和TensorFlow精通預測分析》涵蓋了集成方法的各種實現方式,以及它們如何應用於真實數據集,以提高分類和回歸問題的預測準確性。

本書從集成方法及其特點開始介紹。您將了解到scikit-learn提供了選擇模型超參數的工具。隨著閱讀的進展,您將深入研究預測分析的細節,並探索其特點和特性。您還將介紹人工神經網絡和TensorFlow,以及如何使用它來創建神經網絡。在最後一章中,您將探討計算能力等因素,以及提高預測分析效率的改進方法和軟件增強功能。

通過閱讀本書,您將熟練使用深度神經網絡來解決大數據分析中的常見問題。

您將學到:
- 使用集成算法獲得準確的預測
- 應用降維技術結合特徵並構建更好的模型
- 使用交叉驗證選擇最佳超參數
- 實施不同技術解決預測分析領域的當前挑戰
- 理解深度神經網絡模型的各個元素
- 實施神經網絡解決分類和回歸問題

本書適合對使用Python進行高級預測分析感興趣的數據分析師、軟件工程師和機器學習開發人員。商業智能專家也會發現本書是必不可少的,因為它將教您如何從基本預測模型進一步發展,構建高級模型並產生更準確的預測。預設假設讀者具備Python的基礎知識和對預測分析概念的熟悉。

目錄:
1. 集成方法用於回歸和分類
2. 交叉驗證和參數調整
3. 特徵處理
4. 人工神經網絡和TensorFlow簡介
5. 使用TensorFlow和深度神經網絡進行預測分析