| Description |
1 online resource (426 pages) |
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text file |
| Contents |
Cover; FM; Copyright; Table of Contents; Preface; Chapter 1: Introduction to Data Science and Data Pre-Processing; Introduction; Python Libraries; Roadmap for Building Machine Learning Models; Data Representation; Independent and Target Variables; Exercise 1: Loading a Sample Dataset and Creating the Feature Matrix and Target Matrix; Data Cleaning; Exercise 2: Removing Missing Data; Exercise 3: Imputing Missing Data; Exercise 4: Finding and Removing Outliers in Data; Data Integration; Exercise 5: Integrating Data; Data Transformation; Handling Categorical Data |
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Exercise 6: Simple Replacement of Categorical Data with a NumberExercise 7: Converting Categorical Data to Numerical Data Using Label Encoding; Exercise 8: Converting Categorical Data to Numerical Data Using One-Hot Encoding; Data in Different Scales; Exercise 9: Implementing Scaling Using the Standard Scaler Method; Exercise 10: Implementing Scaling Using the MinMax Scaler Method; Data Discretization; Exercise 11: Discretization of Continuous Data; Train and Test Data; Exercise 12: Splitting Data into Train and Test Sets |
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Activity 1: Pre-Processing Using the Bank Marketing Subscription DatasetSupervised Learning; Unsupervised Learning; Reinforcement Learning; Performance Metrics; Summary; Chapter 2: Data Visualization; Introduction; Functional Approach; Exercise 13: Functional Approach -- Line Plot; Exercise 14: Functional Approach -- Add a Second Line to the Line Plot; Activity 2: Line Plot; Exercise 15: Creating a Bar Plot; Activity 3: Bar Plot; Exercise 16: Functional Approach -- Histogram; Exercise 17: Functional Approach -- Box-and-Whisker plot; Exercise 18: Scatterplot |
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Object-Oriented Approach Using SubplotsExercise 19: Single Line Plot using Subplots; Exercise 20: Multiple Line Plots Using Subplots; Activity 4: Multiple Plot Types Using Subplots; Summary; Chapter 3: Introduction to Machine Learning via Scikit-Learn; Introduction; Introduction to Linear and Logistic Regression; Simple Linear Regression; Exercise 21: Preparing Data for a Linear Regression Model; Exercise 22: Fitting a Simple Linear Regression Model and Determining the Intercept and Coefficient |
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Exercise 23: Generating Predictions and Evaluating the Performance of a Simple Linear Regression ModelMultiple Linear Regression; Exercise 24: Fitting a Multiple Linear Regression Model and Determining the Intercept and Coefficients; Activity 5: Generating Predictions and Evaluating the Performance of a Multiple Linear Regression Model; Logistic Regression; Exercise 25: Fitting a Logistic Regression Model and Determining the Intercept and Coefficients; Exercise 26: Generating Predictions and Evaluating the Performance of a Logistic Regression Model |
| Note |
Exercise 27: Tuning the Hyperparameters of a Multiple Logistic Regression Model |
| Summary |
Data Science with Python will help you get comfortable with using the Python environment for data science. You will learn all the libraries that a data scientist uses on a daily basis. By the end of this course, you will be able to take a large raw dataset, clean it, manipulate it, and run machine learning algorithms to obtain results that ... |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Machine learning.
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Machine learning. |
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Data mining.
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Data mining. |
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Python (Computer program language)
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Python (Computer program language) |
| Genre/Form |
Electronic books.
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| Added Author |
England, Aaron.
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Alaudeen, Mohamed Noordeen.
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| Other Form: |
Print version: Chopra, Rohan. Data Science with Python : Combine Python with Machine Learning Principles to Discover Hidden Patterns in Raw Data. Birmingham : Packt Publishing, Limited, ©2019 9781838552862 |
| ISBN |
1838552162 |
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9781838552169 (electronic book) |
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9781838552862 (paperback) |
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