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The the Data Science Workshop : Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition [electronic resource]
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | So, Anthony. Joseph, Thomas V. John, Robert Thas. Worsley, Andrew. Asare, Samuel. |
| 서명/저자사항 | The the Data Science Workshop[electronic resource] :Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition. |
| 판사항 | 2nd ed. |
| 발행사항 | Birmingham : Packt Publishing, Limited, 2020. |
| 형태사항 | 1 online resource (823 p.) |
| 소장본 주기 | Master record variable field(s) change: 050, 082, 650 - OCLC control number change |
| ISBN | 9781800569409 1800569408 |
| 일반주기 |
Description based upon print version of record.
Correlation Matrix and Visualization |
| 내용주기 | Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Data Science in Python -- Introduction -- Application of Data Science -- What Is Machine Learning? -- Supervised Learning -- Unsupervised Learning -- Reinforcement Learning -- Overview of Python -- Types of Variable -- Numeric Variables -- Text Variables -- Python List -- Python Dictionary -- Exercise 1.01: Creating a Dictionary That Will Contain Machine Learning Algorithms -- Python for Data Science -- The pandas Package -- DataFrame and Series -- CSV Files -- Excel Spreadsheets -- JSON Exercise 2.01: Loading and Preparing the Data for Analysis -- The Correlation Coefficient -- Exercise 2.02: Graphical Investigation of Linear Relationships Using Python -- Exercise 2.03: Examining a Possible Log-Linear Relationship Using Python -- The Statsmodels formula API -- Exercise 2.04: Fitting a Simple Linear Regression Model Using the Statsmodels formula API -- Analyzing the Model Summary -- The Model Formula Language -- Intercept Handling -- Activity 2.01: Fitting a Log-Linear Model Using the Statsmodels Formula API -- Multiple Regression Analysis Exercise 2.05: Fitting a Multiple Linear Regression Model Using the Statsmodels Formula API -- Assumptions of Regression Analysis -- Activity 2.02: Fitting a Multiple Log-Linear Regression Model -- Explaining the Results of Regression Analysis -- Regression Analysis Checks and Balances -- The F-test -- The t-test -- Summary -- Chapter 3: Binary Classification -- Introduction -- Understanding the Business Context -- Business Discovery -- Exercise 3.01: Loading and Exploring the Data from the Dataset -- Testing Business Hypotheses Using Exploratory Data Analysis Visualization for Exploratory Data Analysis -- Exercise 3.02: Business Hypothesis Testing for Age versus Propensity for a Term Loan -- Intuitions from the Exploratory Analysis -- Activity 3.01: Business Hypothesis Testing to Find Employment Status versus Propensity for Term Deposits -- Feature Engineering -- Business-Driven Feature Engineering -- Exercise 3.03: Feature Engineering -- Exploration of Individual Features -- Exercise 3.04: Feature Engineering -- Creating New Features from Existing Ones -- Data-Driven Feature Engineering -- A Quick Peek at Data Types and a Descriptive Summary |
| 요약 | The Data Science Workshop equips you with the basic skills you need to start working on a variety of data science projects. You'll work through the essential building blocks of a data science project gradually through the book, and then put all the pieces together to consolidate your knowledge and apply your learnings in the real world. |
| 일반주제명 | Programming & scripting languages: general. Data capture & analysis. Information visualization. Computers -- Data Processing. Computers -- Programming Languages -- Python. Machine learning. Electronic data processing. Statistics -- Data processing. Python (Computer program language) Application software -- Development. |
| 언어 | 영어 |
| 기타형태 저록 | Print version:So, AnthonyThe the Data Science Workshop : Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd EditionBirmingham : Packt Publishing, Limited,c2020 |
| 대출바로가기 | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2589264 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00018868 | 006.31 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |
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