Data Science with Python : Combine Python with Machine Learning Principles to Discover Hidden Patterns in Raw Data.

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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Chopra, Rohan
Otros Autores: England, Aaron, Alaudeen, Mohamed Noordeen
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, Limited, 2019.
Temas:
Machine learning.
Data mining.
Python (Computer program language)
Apprentissage automatique.
Exploration de données (Informatique)
Python (Langage de programmation)
Data mining
Machine learning
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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

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