"Introduction to Machine Learning in the Cloud with Python" - Information and Links:

Introduction to Machine Learning in the Cloud with Python - Info and Reading Options

Concepts and Practice

"Introduction to Machine Learning in the Cloud with Python" was published by Springer International Publishing AG in 2021 - Cham, it has 1 pages and the language of the book is English.


“Introduction to Machine Learning in the Cloud with Python” Metadata:

  • Title: ➤  Introduction to Machine Learning in the Cloud with Python
  • Authors:
  • Language: English
  • Number of Pages: 1
  • Publisher: ➤  Springer International Publishing AG
  • Publish Date:
  • Publish Location: Cham

Edition Specifications:

  • Weight: 0.623
  • Pagination: xix, 284

Edition Identifiers:

AI-generated Review of “Introduction to Machine Learning in the Cloud with Python”:


"Introduction to Machine Learning in the Cloud with Python" Description:

Open Data:

Intro -- Foreword -- Preface -- About the Book -- Contents -- Acronyms -- Part I: Concepts -- Chapter 1: Machine Learning Concepts -- 1.1 Terminology -- 1.2 What Is Machine Learning? -- 1.2.1 Mitchell´s Notion of Machine Learning -- 1.3 What Does Learning Mean for a Computer? -- 1.4 Difference Between ML and Traditional Programming -- 1.5 How Do Machines Learn? -- 1.6 Steps to Apply ML -- 1.7 Paradigms of Learning -- 1.7.1 Supervised Machine Learning -- 1.7.2 Unsupervised Machine Learning -- 1.7.3 Reinforcement Machine Learning -- 1.7.3.1 Types of Problems in Machine Learning -- 1.8 Machine Learning in Practice -- 1.9 Why Use Machine Learning? -- 1.10 Why Machine Learning Now? -- 1.11 Classical Tasks for Machine Learning -- 1.12 Applications of Machine Learning -- 1.12.1 Applications in Our Daily Life -- 1.13 ML Computing Needs -- 1.14 Machine Learning in the Cloud -- 1.15 Tools Used in Machine Learning -- 1.16 Points to Ponder -- References -- Chapter 2: Machine Learning Algorithms -- 2.1 Why Choose Machine Learning? -- 2.2 Supervised Machine Learning Algorithms -- 2.2.1 Regression -- 2.2.2 Classification -- 2.2.3 Machine Learning Algorithms: Supervised Learning -- 2.2.4 Machine Learning Algorithms: Unsupervised Learning -- 2.2.4.1 Clustering -- 2.2.4.2 Dimension Reduction -- 2.2.4.3 Anomaly Detection -- 2.2.5 Machine Learning Algorithms That Use Unsupervised Learning -- 2.3 Considerations in Choosing an Algorithm -- 2.4 What Are the Most Common and Popular Machine Learning Algorithms? -- 2.4.1 Linear Regression -- 2.4.2 Two Types of Linear Regression -- 2.4.2.1 Simple Linear Regression -- 2.4.2.2 Multiple Linear Regression -- 2.4.2.3 Assumptions of Linear Regression -- 2.4.2.4 Advantages -- 2.4.2.5 Disadvantages -- 2.4.2.6 Sample Python Code for Linear Regression -- 2.4.3 K-Nearest Neighbors (KNN) -- 2.4.3.1 Assumptions

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