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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…mehr
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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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
