Wei-Meng Lee

Python Machine Learning

• Broschiertes Buch

Produktbeschreibung

Python makes machine learning easy for beginners and experienced developers

With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart--it requires a good foundation in statistics, as well as programming knowledge. Python Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today.

Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand.

_ Python data science--manipulating data and data visualization

_ Data cleansing

_ Understanding Machine learning algorithms
_ Supervised learning algorithms

_ Unsupervised learning algorithms

_ Deploying machine learning models

Python Machine Learning is essential reading for students, developers, or anyone with a keen interest in taking their coding skills to the next level.

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Produktdetails

• Verlag: Wiley / Wiley & Sons
• Artikelnr. des Verlages: 1W119545630
• 1. Auflage
• Seitenzahl: 320
• Erscheinungstermin: 30. April 2019
• Englisch
• Abmessung: 249mm x 191mm x 2mm
• Gewicht: 429g
• ISBN-13: 9781119545637
• ISBN-10: 1119545633
• Artikelnr.: 53069030

Autorenporträt

Wei-Meng Lee is a technologist and founder of Developer Learning Solutions (http://www.learn2develop.net), a technology company specializing in hands-on training on the latest mobile technologies. Wei-Meng has many years of training experiences and his training courses place special emphasis on the learning-by-doing approach. His hands-on approach to learning programming makes understanding the subject much easier than reading books, tutorials, and documentations. His name regularly appears in online and print publications such as DevX.com, MobiForge.com, and CoDe Magazine.

Inhaltsangabe

Introduction xxiii

Chapter 1 Introduction to Machine Learning 1

What Is Machine Learning? 2

What Problems Will Machine Learning Be Solving in This Book? 3

Classification 4

Regression 4

Clustering 5

Types of Machine Learning Algorithms 5

Supervised Learning 5

Unsupervised Learning 7

Getting the Tools 8

Obtaining Anaconda 8

Installing Anaconda 9

Running Jupyter Notebook for Mac 9

Running Jupyter Notebook for Windows 10

Creating a New Notebook 11

Naming the Notebook 12

Adding and Removing Cells 13

Running a Cell 14

Restarting the Kernel 16

Exporting Your Notebook 16

Getting Help 17

Chapter 2 Extending Python Using NumPy 19

What Is NumPy? 19

Creating NumPy Arrays 20

Array Indexing 22

Boolean Indexing 22

Slicing Arrays 23

NumPy Slice Is a Reference 25

Reshaping Arrays 26

Array Math 27

Dot Product 29

Matrix 30

Cumulative Sum 31

NumPy Sorting 32

Array Assignment 34

Copying by Reference 34

Copying by View (Shallow Copy) 36

Copying by Value (Deep Copy) 37

Chapter 3 Manipulating Tabular Data Using Pandas 39

What Is Pandas? 39

Pandas Series 40

Creating a Series Using a Specified Index 41

Accessing Elements in a Series 41

Specifying a Datetime Range as the Index of a Series 42

Date Ranges 43

Pandas DataFrame 45

Creating a DataFrame 45

Specifying the Index in a DataFrame 46

Generating Descriptive Statistics on the DataFrame 47

Extracting from DataFrames 49

Selecting the First and Last Five Rows 49

Selecting a Specific Column in a DataFrame 50

Slicing Based on Row Number 50

Slicing Based on Row and Column Numbers 51

Slicing Based on Labels 52

Selecting a Single Cell in a DataFrame 54

Selecting Based on Cell Value 54

Transforming DataFrames 54

Checking to See If a Result Is a DataFrame or Series 55

Sorting Data in a DataFrame 55

Sorting by Index 55

Sorting by Value 56

Applying Functions to a DataFrame 57

Adding and Removing Rows and Columns in a DataFrame 60

Adding a Column 61

Removing Rows 61

Removing Columns 62

Generating a Crosstab 63

Chapter 4 Data Visualization Using matplotlib 67

What Is matplotlib? 67

Plotting Line Charts 68

Adding Title and Labels 69

Styling 69

Plotting Multiple Lines in the Same Chart 71

Adding a Legend 72

Plotting Bar Charts 73

Adding Another Bar to the Chart 74

Changing the Tick Marks 75

Plotting Pie Charts 77

Exploding the Slices 78

Displaying Custom Colors 79

Rotating the Pie Chart 80

Displaying a Legend 81

Saving the Chart 82

Plotting Scatter Plots 83

Combining Plots 83

Subplots 84

Plotting Using Seaborn 85

Displaying Categorical Plots 86

Displaying Lmplots 88

Displaying Swarmplots 90

Chapter 5 Getting Started with Scikit-learn for Machine Learning 93

Introduction to Scikit-learn 93

Getting Datasets 94

Using the Scikit-learn Dataset 94

Using the Kaggle Dataset 97

Using the U