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...
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| Main Author: | |
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| Other Authors: | , , , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham :
Packt Publishing, Limited,
2020.
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| Edition: | 2nd ed. |
| Subjects: | |
| Online Access: | CONNECT |
MARC
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| 100 | 1 | |a So, Anthony |c (Data scientist) |1 https://id.oclc.org/worldcat/entity/E39PCjGVCDWxcCx8xrFc47wmr3 | |
| 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) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 588 | 0 | |a Print version record. | |
| 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. | ||
| 500 | |a EBSCO eBook Academic Comprehensive Collection North America |5 TMurS | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Electronic data processing. | |
| 650 | 0 | |a Statistics |x Data processing. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Application software |x Development. | |
| 700 | 1 | |a Joseph, Thomas V. | |
| 700 | 1 | |a John, Robert Thas. | |
| 700 | 1 | |a Worsley, Andrew. | |
| 700 | 1 | |a Asare, Samuel. | |
| 730 | 0 | |a WORLDSHARE SUB RECORDS | |
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| 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 |
| 856 | 4 | 0 | |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2589264&authtype=ip,sso&custid=s4672406 |z CONNECT |3 eBooks on EBSCOhost |t 0 |
| 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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| 952 | f | f | |a Middle Tennessee State University |b Main |c James E. Walker Library |d Electronic Resources |t 0 |e Q325.5 .S62 2020b |h Library of Congress classification |
