Feature Engineering Bookcamp

In Feature Engineering Bookcamp you will learn how to:

Identify and implement feature transformations for your data
Build powerful machine learning pipelines with unstructured data like text and images
Quantify and minimize bias in machine learning pipelines at the data level
Use feature stores to build real-time feature engineering pipelines
Enhance existing machine learning pipelines by manipulating the input data
Use state-of-the-art deep learning models to extract hidden patterns in data

Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. You'll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your model's performance. This instantly-useful book skips the abstract mathematical theory and minutely-detailed formulas; instead you'll learn through interesting code-driven case studies, including tweet classification, COVID detection, recidivism prediction, stock price movement detection, and more.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
Get better output from machine learning pipelines by improving your training data! Use feature engineering, a machine learning technique for designing relevant input variables based on your existing data, to simplify training and enhance model performance. While fine-tuning hyperparameters or tweaking models may give you a minor performance bump, feature engineering delivers dramatic improvements by transforming your data pipeline.

About the book
Feature Engineering Bookcamp walks you through six hands-on projects where you'll learn to upgrade your training data using feature engineering. Each chapter explores a new code-driven case study, taken from real-world industries like finance and healthcare. You'll practice cleaning and transforming data, mitigating bias, and more. The book is full of performance-enhancing tips for all major ML subdomains--from natural language processing to time-series analysis.

What's inside

Identify and implement feature transformations
Build machine learning pipelines with unstructured data
Quantify and minimize bias in ML pipelines
Use feature stores to build real-time feature engineering pipelines
Enhance existing pipelines by manipulating input data

About the reader
For experienced machine learning engineers familiar with Python.

About the author
Sinan Ozdemir is the founder and CTO of Shiba, a former lecturer of Data Science at Johns Hopkins University, and the author of multiple textbooks on data science and machine learning.

Table of Contents
1 Introduction to feature engineering
2 The basics of feature engineering
3 Healthcare: Diagnosing COVID-19
4 Bias and fairness: Modeling recidivism
5 Natural language processing: Classifying social media sentiment
6 Computer vision: Object recognition
7 Time series analysis: Day trading with machine learning
8 Feature stores
9 Putting it all together

在不花費數小時微調參數的情況下，為您的機器學習流程帶來巨大改進！本書的實用案例研究揭示了升級數據整理和機器學習結果的特徵工程技術。

在《特徵工程訓練營》中，您將學習以下內容：
- 為您的數據識別和實施特徵轉換
- 使用文本和圖像等非結構化數據構建強大的機器學習流程
- 在數據層面量化和減少機器學習流程中的偏差
- 使用特徵存儲庫構建實時特徵工程流程
- 通過操作輸入數據增強現有的機器學習流程
- 使用最先進的深度學習模型提取數據中的隱藏模式

《特徵工程訓練營》將引導您進行一系列項目，讓您親自實踐核心特徵工程技術。您將使用加速數據處理時間並改善模型性能的特徵工程實踐。這本立即可用的書籍跳過了抽象的數學理論和細節繁瑣的公式，而是通過有趣的代碼驅動案例研究來學習，包括推文分類、COVID檢測、再犯預測、股價變動檢測等。

購買印刷版書籍將包含Manning Publications提供的PDF、Kindle和ePub格式的免費電子書。

關於技術：
通過改進訓練數據，從機器學習流程中獲得更好的輸出！使用特徵工程，一種基於現有數據設計相關輸入變量的機器學習技術，簡化訓練並提高模型性能。儘管微調超參數或調整模型可能會稍微提高性能，但通過轉換數據流程，特徵工程可以實現顯著的改進。

關於本書：
《特徵工程訓練營》將引導您通過特徵工程來升級訓練數據，並通過六個實踐項目進行實踐。每一章都探索了來自金融和醫療等現實世界行業的新的代碼驅動案例研究。您將練習數據清理和轉換、減少偏差等技術。本書充滿了提高性能的技巧，適用於所有主要的機器學習子領域，從自然語言處理到時間序列分析。

內容簡介：
- 識別和實施特徵轉換
- 使用非結構化數據構建機器學習流程
- 量化和減少機器學習流程中的偏差
- 使用特徵存儲庫構建實時特徵工程流程
- 通過操作輸入數據增強現有流程

讀者對象：
熟悉Python的經驗豐富的機器學習工程師。

關於作者：
Sinan Ozdemir是Shiba的創始人兼首席技術官，曾任約翰霍普金斯大學的數據科學講師，並撰寫了多本關於數據科學和機器學習的教科書。

目錄：
1. 特徵工程簡介
2. 特徵工程基礎
3. 醫療保健：診斷COVID-19
4. 偏見和公平性：建模再犯
5. 自然語言處理：社交媒體情感分類
6. 計算機視覺：物體識別
7. 時間序列分析：使用機器學習進行日內交易
8. 特徵存儲庫
9. 綜合應用