Mastering Machine Learning with Python in Six Steps, Second Edition: A Practical Implementation Guide to Predictive Data Analytics Using Python

Swamynathan, Manohar

  • 出版商: Apress
  • 出版日期: 2019-10-02
  • 售價: $1,700
  • 貴賓價: 9.5$1,615
  • 語言: 英文
  • 頁數: 370
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1484249461
  • ISBN-13: 9781484249468
  • 相關分類: Python程式語言Machine LearningData Science
  • 立即出貨 (庫存=1)

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商品描述

Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version's approach is based on the "six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages.

You'll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You'll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data.

Finally, you'll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.

What You'll Learn

  • Understand machine learning development and frameworks
  • Assess model diagnosis and tuning in machine learning
  • Examine text mining, natuarl language processing (NLP), and recommender systems
  • Review reinforcement learning and CNN

Who This Book Is For

Python developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.


商品描述(中文翻譯)

在這本書中,你將透過六個步驟,從基礎到高階的 Python 3 主題,成為一位優秀的實踐者。這個更新版本的方法基於「六度分隔理論」,該理論認為每個人和每件事最多只相隔六個步驟,並將每個主題分為兩個部分:理論概念和使用適當的 Python 3 套件進行實際實施。

你將從 Python 3 程式語言的基礎、機器學習的歷史、演進和系統開發框架開始。同時還涵蓋了關鍵的資料探勘/分析概念,例如探索性分析、特徵維度縮減、回歸、時間序列預測以及在 Scikit-learn 中的高效實現。你還將學習常用的模型診斷和調整技術,包括用於類別創建的最佳概率截點、變異、偏差、裝袋、提升、集成投票、網格搜索、隨機搜索、貝葉斯優化以及用於物聯網數據的噪音降低技術。

最後,你將回顧高級的文本探勘技術、推薦系統、神經網絡、深度學習、強化學習技術及其實現。書中提供的所有代碼都以 iPython 筆記本的形式提供,讓你可以嘗試這些示例並將其擴展到你的優勢領域。

你將學到什麼:
- 瞭解機器學習的發展和框架
- 評估機器學習中的模型診斷和調整
- 探討文本探勘、自然語言處理(NLP)和推薦系統
- 回顧強化學習和卷積神經網絡(CNN)

適合閱讀對象:
- Python 開發人員、資料工程師和機器學習工程師,希望擴展他們的知識或職業發展到機器學習領域。

作者簡介

Manohar Swamynathan is a data science practitioner and an avid programmer, with over 14+ years of experience in various data science related areas that include data warehousing, Business Intelligence (BI), analytical tool development, ad-hoc analysis, predictive modeling, data science product development, consulting, formulating strategy and executing analytics program. He's had a career covering life cycle of data across different domains such as US mortgage banking, retail/e-commerce, insurance, and industrial IoT. He has a bachelor's degree with a specialization in physics, mathematics, computers, and a master's degree in project management. He's currently living in Bengaluru, the silicon valley of India.

作者簡介(中文翻譯)

Manohar Swamynathan 是一位資料科學實踐者和熱衷的程式設計師,擁有超過14年的經驗,涵蓋了數據倉儲、商業智能(BI)、分析工具開發、即席分析、預測建模、資料科學產品開發、諮詢、制定策略和執行分析計劃等相關領域。他的職業生涯涵蓋了美國抵押銀行、零售/電子商務、保險和工業物聯網等不同領域的數據生命週期。他擁有物理學、數學、計算機的學士學位,以及項目管理的碩士學位。他目前居住在印度的硅谷─班加羅爾。