Python Machine Learning.

Lee, Wei-Meng.
Newark : John Wiley & Sons, Incorporated, 2019.
1 online resource (323 pages)

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Python (Computer program language).
Electronic books.
Title Page
About the Author
About the Technical Editor
Contents at a glance
Chapter 1 Introduction to Machine Learning
What Is Machine Learning?
What Problems Will Machine Learning Be Solving in This Book?
Types of Machine Learning Algorithms
Supervised Learning
Unsupervised Learning
Getting the Tools
Obtaining Anaconda
Installing Anaconda
Running Jupyter Notebook for Mac
Running Jupyter Notebook for Windows
Creating a New Notebook
Naming the Notebook
Adding and Removing Cells
Running a Cell
Restarting the Kernel
Exporting Your Notebook
Getting Help
Chapter 2 Extending Python Using NumPy
What Is NumPy?
Creating NumPy Arrays
Array Indexing
Boolean Indexing
Slicing Arrays
NumPy Slice Is a Reference
Reshaping Arrays
Array Math
Dot Product
Cumulative Sum
NumPy Sorting
Array Assignment
Copying by Reference
Copying by View (Shallow Copy)
Copying by Value (Deep Copy)
Chapter 3 Manipulating Tabular Data Using Pandas
What Is Pandas?
Pandas Series
Creating a Series Using a Specified Index
Accessing Elements in a Series
Specifying a Datetime Range as the Index of a Series
Date Ranges
Pandas DataFrame
Creating a DataFrame
Specifying the Index in a DataFrame
Generating Descriptive Statistics on the DataFrame
Extracting from DataFrames
Selecting the First and Last Five Rows
Selecting a Specific Column in a DataFrame
Slicing Based on Row Number
Slicing Based on Row and Column Numbers
Slicing Based on Labels
Selecting a Single Cell in a DataFrame
Selecting Based on Cell Value
Transforming DataFrames.
Checking to See If a Result Is a DataFrame or Series
Sorting Data in a DataFrame
Sorting by Index
Sorting by Value
Applying Functions to a DataFrame
Adding and Removing Rows and Columns in a DataFrame
Adding a Column
Removing Rows
Removing Columns
Generating a Crosstab
Chapter 4 Data Visualization Using matplotlib
What Is matplotlib?
Plotting Line Charts
Adding Title and Labels
Plotting Multiple Lines in the Same Chart
Adding a Legend
Plotting Bar Charts
Adding Another Bar to the Chart
Changing the Tick Marks
Plotting Pie Charts
Exploding the Slices
Displaying Custom Colors
Rotating the Pie Chart
Displaying a Legend
Saving the Chart
Plotting Scatter Plots
Combining Plots
Plotting Using Seaborn
Displaying Categorical Plots
Displaying Lmplots
Displaying Swarmplots
Chapter 5 Getting Started with Scikit-learn for Machine Learning
Introduction to Scikit-learn
Getting Datasets
Using the Scikit-learn Dataset
Using the Kaggle Dataset
Using the UCI (University of California, Irvine) Machine Learning Repository
Generating Your Own Dataset
Linearly Distributed Dataset
Clustered Dataset
Clustered Dataset Distributed in Circular Fashion
Getting Started with Scikit-learn
Using the LinearRegression Class for Fitting the Model
Making Predictions
Plotting the Linear Regression Line
Getting the Gradient and Intercept of the Linear Regression Line
Examining the Performance of the Model by Calculating the Residual Sum of Squares
Evaluating the Model Using a Test Dataset
Persisting the Model
Data Cleansing
Cleaning Rows with NaNs
Replacing NaN with the Mean of the Column
Removing Rows
Removing Duplicate Rows
Normalizing Columns
Removing Outliers
Tukey Fences.
Chapter 6 Supervised Learning-Linear Regression
Types of Linear Regression
Linear Regression
Using the Boston Dataset
Data Cleansing
Feature Selection
Multiple Regression
Training the Model
Getting the Intercept and Coefficients
Plotting the 3D Hyperplane
Polynomial Regression
Formula for Polynomial Regression
Polynomial Regression in Scikit-learn
Understanding Bias and Variance
Using Polynomial Multiple Regression on the Boston Dataset
Plotting the 3D Hyperplane
Chapter 7 Supervised Learning-Classification Using Logistic Regression
What Is Logistic Regression?
Understanding Odds
Logit Function
Sigmoid Curve
Using the Breast Cancer Wisconsin (Diagnostic) Data Set
Examining the Relationship Between Features
Plotting the Features in 2D
Plotting in 3D
Training Using One Feature
Finding the Intercept and Coefficient
Plotting the Sigmoid Curve
Making Predictions
Training the Model Using All Features
Testing the Model
Getting the Confusion Matrix
Computing Accuracy, Recall, Precision, and Other Metrics
Receiver Operating Characteristic (ROC) Curve
Plotting the ROC and Finding the Area Under the Curve (AUC)
Chapter 8 Supervised Learning-Classification Using Support Vector Machines
What Is a Support Vector Machine?
Maximum Separability
Support Vectors
Formula for the Hyperplane
Using Scikit-learn for SVM
Plotting the Hyperplane and the Margins
Making Predictions
Kernel Trick
Adding a Third Dimension
Plotting the 3D Hyperplane
Types of Kernels
Radial Basis Function (RBF) Kernel
Polynomial Kernel
Using SVM for Real-Life Problems
Chapter 9 Supervised Learning-Classification Using K-Nearest Neighbors (KNN)
What Is K-Nearest Neighbors?.
Implementing KNN in Python
Plotting the Points
Calculating the Distance Between the Points
Implementing KNN
Making Predictions
Visualizing Different Values of K
Using Scikit-Learn's KNeighborsClassifier Class for KNN
Exploring Different Values of K
Parameter-Tuning K
Finding the Optimal K
Chapter 10 Unsupervised Learning-Clustering Using K-Means
What Is Unsupervised Learning?
Unsupervised Learning Using K-Means
How Clustering in K-Means Works
Implementing K-Means in Python
Using K-Means in Scikit-learn
Evaluating Cluster Size Using the Silhouette Coefficient
Calculating the Silhouette Coefficient
Finding the Optimal K
Using K-Means to Solve Real-Life Problems
Importing the Data
Cleaning the Data
Plotting the Scatter Plot
Clustering Using K-Means
Finding the Optimal Size Classes
Chapter 11 Using Azure Machine Learning Studio
What Is Microsoft Azure Machine Learning Studio?
An Example Using the Titanic Experiment
Using Microsoft Azure Machine Learning Studio
Uploading Your Dataset
Creating an Experiment
Filtering the Data and Making Fields Categorical
Removing the Missing Data
Splitting the Data for Training and Testing
Training a Model
Comparing Against Other Algorithms
Evaluating Machine Learning Algorithms
Publishing the Learning Model as a Web Service
Publishing the Experiment
Testing the Web Service
Programmatically Accessing the Web Service
Chapter 12 Deploying Machine Learning Models
Deploying ML
Case Study
Loading the Data
Cleaning the Data
Examining the Correlation Between the Features
Plotting the Correlation Between Features
Evaluating the Algorithms
Logistic Regression
K-Nearest Neighbors
Support Vector Machines.
Selecting the Best Performing Algorithm
Training and Saving the Model
Deploying the Model
Testing the Model
Creating the Client Application to Use the Model
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Local notes:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2021. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Other format:
Print version: Lee, Wei-Meng Python Machine Learning