Introduction to machine learning in the cloud with Python: concepts and practices - Info and Reading Options
By Pramod Chandra Gupta and Naresh Kumar Sehgal
"Introduction to machine learning in the cloud with Python: concepts and practices" was published by Springer in 2021 - Cham and it has 1 pages.
“Introduction to machine learning in the cloud with Python: concepts and practices” Metadata:
- Title: ➤ Introduction to machine learning in the cloud with Python: concepts and practices
- Authors: Pramod Chandra GuptaNaresh Kumar Sehgal
- Number of Pages: 1
- Publisher: Springer
- Publish Date: 2021
- Publish Location: Cham
Edition Identifiers:
- ISBN-13: 9783030712709
- All ISBNs: 9783030712709
AI-generated Review of “Introduction to machine learning in the cloud with Python: concepts and practices”:
"Introduction to machine learning in the cloud with Python: concepts and practices" 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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