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Data Science with Python : Combine Python with Machine Learning Principles to Discover Hidden Patterns in Raw Data.
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 l...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Chopra, Rohan. | |
| 245 | 1 | 0 | |a Data Science with Python : |b Combine Python with Machine Learning Principles to Discover Hidden Patterns in Raw Data. |
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2019. | ||
| 300 | |a 1 online resource (426 pages) | ||
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| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 500 | |a Exercise 27: Tuning the Hyperparameters of a Multiple Logistic Regression Model | ||
| 520 | |a 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 ... | ||
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| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 700 | 1 | |a England, Aaron. | |
| 700 | 1 | |a Alaudeen, Mohamed Noordeen. | |
| 776 | 0 | 8 | |i Print version: |a Chopra, Rohan. |t Data Science with Python : Combine Python with Machine Learning Principles to Discover Hidden Patterns in Raw Data. |d Birmingham : Packt Publishing, Limited, ©2019 |z 9781838552862 |
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