Statistics for Machine Learning

Pratap Dangeti

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商品描述

Key Features

  • Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics.
  • Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.
  • Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.

Book Description

Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.

By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.

What you will learn

  • Understand the Statistical and Machine Learning fundamentals necessary to build models
  • Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems
  • Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages
  • Analyze the results and tune the model appropriately to your own predictive goals
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商品描述(中文翻譯)

主要特點

- 透過 p-value、ANOVA 和 F-統計量,了解強大預測模型背後的統計學知識。
- 透過 K-means 聚類,以程式化方式實現監督式和非監督式學習的統計計算。
- 通過這本範例豐富的 R 和 Python 指南,掌握機器學習的統計學方面。

書籍描述

機器學習中的複雜統計學問題常常讓開發者感到困擾。了解統計學可以幫助您建立針對特定問題陳述進行優化的強大機器學習模型。本書將教您執行機器學習所需的複雜統計計算。您將獲得有關監督式學習、非監督式學習、強化學習等背後統計學的資訊。了解討論機器學習統計學方面的實際案例,並熟悉它。您還將設計用於執行模型、參數擬合、回歸、分類、密度收集等任務的程式。

通過閱讀本書,您將掌握機器學習所需的統計學知識,並能將新技能應用於任何行業問題。

您將學到什麼

- 瞭解構建模型所需的統計學和機器學習基礎知識。
- 瞭解解決問題的統計學方法和機器學習方法之間的主要差異和相似之處。
- 通過使用充足的 R 和 Python 套件,學習如何準備數據並為模型提供輸入。
- 分析結果並根據自己的預測目標適當地調整模型。