Statistics for Machine Learning: Implement Statistical Methods Used in Machine Learning Using Python (English Edition)
暫譯: 機器學習的統計學：使用 Python 實現機器學習中的統計方法（英文版）
Singh, Himanshu
- 出版商: BPB Publications
- 出版日期: 2021-01-15
- 售價: $1,130
- 貴賓價: 9.5 折 $1,074
- 語言: 英文
- 頁數: 280
- 裝訂: Quality Paper - also called trade paper
- ISBN: 9388511972
- ISBN-13: 9789388511971
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相關分類:
Python、程式語言、Machine Learning、機率統計學 Probability-and-statistics
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相關主題
商品描述
A practical guide that will help you understand the Statistical Foundations of any Machine Learning Problem.
Key Features Develop a Conceptual and Mathematical understanding of Statistics Get an overview of Statistical Applications in Python Learn how to perform Hypothesis testing in Statistics Understand why Statistics is important in Machine Learning Learn how to process data in Python
Description
This book talks about Statistical concepts in detail, with its applications in Python. The book starts with an introduction to Statistics and moves on to cover some basic Descriptive Statistics concepts such as mean, median, mode, etc. You will then explore the concept of Probability and look at different types of Probability Distributions. Next, you will look at parameter estimations for the unknown parameters present in the population and look at Random Variables in detail, which are used to save the results of an experiment in Statistics. You will then explore one of the most important fields in Statistics - Hypothesis Testing, and then explore various types of tests used to check our hypothesis. The last part of our book will focus on how you can process data using Python, some elements of Non-parametric statistics, and finally, some introduction to Machine Learning.
What you will learn
Understand the basics of Statistics Get to know more about Descriptive Statistics Understand and learn advanced Statistics techniques Learn how to apply Statistical concepts in Python Understand important Python packages for Statistics and Machine Learning
Who this book is for
This book is for anyone who wants to understand Statistics and its use in Machine Learning. This book will help you understand the Mathematics behind the Statistical concepts and the applications using the Python language. Having a working knowledge of the Python language is a prerequisite.
Table of Contents
1. Introduction to Statistics
2. Descriptive Statistics
3. Probability
4. Random Variables
5. Parameter Estimations
6. Hypothesis Testing
7. Analysis of Variance
8. Regression
9. Non Parametric Statistics
10. Data Analysis using Python
11. Introduction to Machine Learning
About the Authors
Himanshu Singh is an AI Technology Lead at Legato Healthcare (An Anthem Inc. Company). He has around 7 years of experience in the domain of Machine Learning and Artificial Intelligence. Himanshu is an author of three books in Machine Learning and is a trainer by passion. He is a guest faculty at various institutes like Narsee Monjee Institute of Management Studies, IMT, Vignana Jyothi Institute of Management.
LinkedIn Profile: https: //www.linkedin.com/in/himanshu-singh-2264a350/
Blog links: https: //medium.com/@himanshuit3036
Facebook Profile: https: //www.facebook.com/silli23
Key Features
Description
This book talks about Statistical concepts in detail, with its applications in Python. The book starts with an introduction to Statistics and moves on to cover some basic Descriptive Statistics concepts such as mean, median, mode, etc. You will then explore the concept of Probability and look at different types of Probability Distributions. Next, you will look at parameter estimations for the unknown parameters present in the population and look at Random Variables in detail, which are used to save the results of an experiment in Statistics. You will then explore one of the most important fields in Statistics - Hypothesis Testing, and then explore various types of tests used to check our hypothesis. The last part of our book will focus on how you can process data using Python, some elements of Non-parametric statistics, and finally, some introduction to Machine Learning.
What you will learn
Who this book is for
This book is for anyone who wants to understand Statistics and its use in Machine Learning. This book will help you understand the Mathematics behind the Statistical concepts and the applications using the Python language. Having a working knowledge of the Python language is a prerequisite.
Table of Contents
1. Introduction to Statistics
2. Descriptive Statistics
3. Probability
4. Random Variables
5. Parameter Estimations
6. Hypothesis Testing
7. Analysis of Variance
8. Regression
9. Non Parametric Statistics
10. Data Analysis using Python
11. Introduction to Machine Learning
About the Authors
Himanshu Singh is an AI Technology Lead at Legato Healthcare (An Anthem Inc. Company). He has around 7 years of experience in the domain of Machine Learning and Artificial Intelligence. Himanshu is an author of three books in Machine Learning and is a trainer by passion. He is a guest faculty at various institutes like Narsee Monjee Institute of Management Studies, IMT, Vignana Jyothi Institute of Management.
LinkedIn Profile: https: //www.linkedin.com/in/himanshu-singh-2264a350/
Blog links: https: //medium.com/@himanshuit3036
Facebook Profile: https: //www.facebook.com/silli23
商品描述(中文翻譯)
一本實用指南，幫助您理解任何機器學習問題的統計基礎。
主要特點
描述
本書詳細討論了統計概念及其在Python中的應用。書籍從統計學的介紹開始，接著涵蓋一些基本的描述性統計概念，如平均數、中位數、眾數等。然後，您將探索概率的概念，並查看不同類型的概率分佈。接下來，您將詳細了解用於保存統計實驗結果的隨機變數，以及對於人口中未知參數的參數估計。然後，您將探索統計學中最重要的領域之一——假設檢驗，並探討用於檢查我們假設的各種測試。書籍的最後部分將重點介紹如何使用Python處理數據、一些非參數統計的元素，最後簡介機器學習。
您將學到什麼
本書適合誰
本書適合任何想要理解統計及其在機器學習中應用的人。本書將幫助您理解統計概念背後的數學及使用Python語言的應用。具備Python語言的基本知識是前提條件。
目錄
1. 統計學介紹
2. 描述性統計
3. 概率
4. 隨機變數
5. 參數估計
6. 假設檢驗
7. 方差分析
8. 回歸
9. 非參數統計
10. 使用Python進行數據分析
11. 機器學習簡介
關於作者
Himanshu Singh 是Legato Healthcare（安森美公司）的一名AI技術主管。他在機器學習和人工智慧領域擁有約7年的經驗。Himanshu是三本機器學習書籍的作者，並且熱愛擔任培訓師。他是多所學院的客座講師，如Narsee Monjee管理研究所、IMT、Vignana Jyothi管理學院。
LinkedIn 個人檔案: https: //www.linkedin.com/in/himanshu-singh-2264a350/
部落格連結: https: //medium.com/@himanshuit3036
Facebook 個人檔案: https: //www.facebook.com/silli23
