The the Data Science Workshop :: Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition.
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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , , , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing, Limited,
2020.
|
| Ausgabe: | 2nd ed. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | Correlation Matrix and Visualization |
| Beschreibung: | 1 online resource (823 pages) |
| ISBN: | 9781800569409 1800569408 |
Internformat
MARC
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| 245 | 1 | 4 | |a The the Data Science Workshop : |b Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition. |
| 250 | |a 2nd ed. | ||
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2020. | ||
| 300 | |a 1 online resource (823 pages) | ||
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| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 500 | |a Correlation Matrix and Visualization | ||
| 520 | |a 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. | ||
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Electronic data processing. |0 http://id.loc.gov/authorities/subjects/sh85042288 | |
| 650 | 0 | |a Statistics |x Data processing. |0 http://id.loc.gov/authorities/subjects/sh85127583 | |
| 650 | 0 | |a Python (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh96008834 | |
| 650 | 0 | |a Application software |x Development. |0 http://id.loc.gov/authorities/subjects/sh95009362 | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Statistique |x Informatique. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Logiciels d'application |x Développement. | |
| 650 | 7 | |a Programming & scripting languages: general. |2 bicssc | |
| 650 | 7 | |a Data capture & analysis. |2 bicssc | |
| 650 | 7 | |a Information visualization. |2 bicssc | |
| 650 | 7 | |a Computers |x Data Processing. |2 bisacsh | |
| 650 | 7 | |a Computers |x Programming Languages |x Python. |2 bisacsh | |
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| 650 | 7 | |a Machine learning |2 fast | |
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| 650 | 7 | |a Statistics |x Data processing |2 fast | |
| 700 | 1 | |a Joseph, Thomas V. | |
| 700 | 1 | |a John, Robert Thas. | |
| 700 | 1 | |a Worsley, Andrew. | |
| 700 | 1 | |a Asare, Samuel. | |
| 758 | |i has work: |a The data science workshop (Text) |1 https://id.oclc.org/worldcat/entity/E39PCG6BHRTRwf6yjPBkjfgBj3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a So, Anthony. |t Data Science Workshop : Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition. |d Birmingham : Packt Publishing, Limited, ©2020 |
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| 880 | 8 | |6 505-00/(S |a Exercise 1.02: Loading Data of Different Formats into a pandas DataFrame -- Scikit-Learn -- What Is a Model-- Model Hyperparameters -- The sklearn API -- Exercise 1.03: Predicting Breast Cancer from a Dataset Using sklearn -- Activity 1.01: Train a Spam Detector Algorithm -- Summary -- Chapter 2: Regression -- Introduction -- Simple Linear Regression -- The Method of Least Squares -- Multiple Linear Regression -- Estimating the Regression Coefficients (β0, β1, β2 and β3) -- Logarithmic Transformations of Variables -- Correlation Matrices -- Conducting Regression Analysis Using Python | |
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| contents | 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 |
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| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | 2nd ed. |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:30:03Z |
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| language | English |
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| spelling | So, Anthony (Data scientist) https://id.oclc.org/worldcat/entity/E39PCjGVCDWxcCx8xrFc47wmr3 http://id.loc.gov/authorities/names/no2021117553 The the Data Science Workshop : Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition. 2nd ed. Birmingham : Packt Publishing, Limited, 2020. 1 online resource (823 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. 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 Correlation Matrix and Visualization 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. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Statistics Data processing. http://id.loc.gov/authorities/subjects/sh85127583 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Apprentissage automatique. Statistique Informatique. Python (Langage de programmation) Logiciels d'application Développement. Programming & scripting languages: general. bicssc Data capture & analysis. bicssc Information visualization. bicssc Computers Data Processing. bisacsh Computers Programming Languages Python. bisacsh Application software Development fast Electronic data processing fast Machine learning fast Python (Computer program language) fast Statistics Data processing fast Joseph, Thomas V. John, Robert Thas. Worsley, Andrew. Asare, Samuel. has work: The data science workshop (Text) https://id.oclc.org/worldcat/entity/E39PCG6BHRTRwf6yjPBkjfgBj3 https://id.oclc.org/worldcat/ontology/hasWork Print version: So, Anthony. Data Science Workshop : Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition. Birmingham : Packt Publishing, Limited, ©2020 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2589264 Volltext 505-00/(S Exercise 1.02: Loading Data of Different Formats into a pandas DataFrame -- Scikit-Learn -- What Is a Model-- Model Hyperparameters -- The sklearn API -- Exercise 1.03: Predicting Breast Cancer from a Dataset Using sklearn -- Activity 1.01: Train a Spam Detector Algorithm -- Summary -- Chapter 2: Regression -- Introduction -- Simple Linear Regression -- The Method of Least Squares -- Multiple Linear Regression -- Estimating the Regression Coefficients (β0, β1, β2 and β3) -- Logarithmic Transformations of Variables -- Correlation Matrices -- Conducting Regression Analysis Using Python |
| spellingShingle | So, Anthony (Data scientist) The the Data Science Workshop : Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition. 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 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Statistics Data processing. http://id.loc.gov/authorities/subjects/sh85127583 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Apprentissage automatique. Statistique Informatique. Python (Langage de programmation) Logiciels d'application Développement. Programming & scripting languages: general. bicssc Data capture & analysis. bicssc Information visualization. bicssc Computers Data Processing. bisacsh Computers Programming Languages Python. bisacsh Application software Development fast Electronic data processing fast Machine learning fast Python (Computer program language) fast Statistics Data processing fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85042288 http://id.loc.gov/authorities/subjects/sh85127583 http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh95009362 |
| title | The the Data Science Workshop : Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition. |
| title_auth | The the Data Science Workshop : Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition. |
| title_exact_search | The the Data Science Workshop : Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition. |
| title_full | The the Data Science Workshop : Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition. |
| title_fullStr | The the Data Science Workshop : Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition. |
| title_full_unstemmed | The the Data Science Workshop : Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition. |
| title_short | The the Data Science Workshop : |
| title_sort | the data science workshop learn how you can build machine learning models and create your own real world data science projects 2nd edition |
| title_sub | Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition. |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Statistics Data processing. http://id.loc.gov/authorities/subjects/sh85127583 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Apprentissage automatique. Statistique Informatique. Python (Langage de programmation) Logiciels d'application Développement. Programming & scripting languages: general. bicssc Data capture & analysis. bicssc Information visualization. bicssc Computers Data Processing. bisacsh Computers Programming Languages Python. bisacsh Application software Development fast Electronic data processing fast Machine learning fast Python (Computer program language) fast Statistics Data processing fast |
| topic_facet | Machine learning. Electronic data processing. Statistics Data processing. Python (Computer program language) Application software Development. Apprentissage automatique. Statistique Informatique. Python (Langage de programmation) Logiciels d'application Développement. Programming & scripting languages: general. Data capture & analysis. Information visualization. Computers Data Processing. Computers Programming Languages Python. Application software Development Electronic data processing Machine learning Statistics Data processing |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2589264 |
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