Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics (Paperback)

Nield, Thomas

Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics (Paperback)

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商品描述

Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career.

Learn how to:

  • Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning
  • Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon
  • Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance
  • Manipulate vectors and matrices and perform matrix decomposition
  • Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks
  • Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market

商品描述(中文翻譯)

掌握在資料科學、機器學習和統計學中所需的數學知識。在這本書中，作者Thomas Nield將引導您深入研究微積分、概率、線性代數和統計學等領域，以及它們如何應用於線性回歸、邏輯回歸和神經網絡等技術。在此過程中，您還將獲得有關資料科學的實用見解，以及如何利用這些見解來最大化您的職業發展。

學習如何：
- 使用Python程式碼和庫（如SymPy、NumPy和scikit-learn）來探索微積分、線性代數、統計學和機器學習等基本數學概念
- 以簡單易懂的方式理解線性回歸、邏輯回歸和神經網絡等技術，盡量避免數學符號和術語
- 對數據集進行描述性統計和假設檢驗，解釋p值和統計顯著性
- 操作向量和矩陣，進行矩陣分解
- 積極學習微積分、概率、統計學和線性代數等知識，並將其應用於包括神經網絡在內的回歸模型
- 在資料科學職業生涯中實際操作，避免常見的陷阱、假設和偏見，同時調整技能組合以在就業市場中脫穎而出

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