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by Wei-Meng Lee
Python Machine Learning
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Introduction
CHAPTER 1: Introduction to Machine Learning
What Is Machine Learning?
Getting the Tools
Summary
CHAPTER 2: Extending Python Using NumPy
What Is NumPy?
Creating NumPy Arrays
Array Indexing
Reshaping Arrays
Array Math
Array Assignment
Summary
CHAPTER 3: Manipulating Tabular Data Using Pandas
What Is Pandas?
Pandas Series
Pandas DataFrame
Summary
CHAPTER 4: Data Visualization Using matplotlib
What Is matplotlib?
Plotting Line Charts
Plotting Bar Charts
Plotting Pie Charts
Plotting Scatter Plots
Plotting Using Seaborn
Summary
CHAPTER 5: Getting Started with Scikit‐learn for Machine Learning
Introduction to Scikit‐learn
Getting Datasets
Getting Started with Scikit‐learn
Data Cleansing
Summary
CHAPTER 6: Supervised Learning—Linear Regression
Types of Linear Regression
Linear Regression
Polynomial Regression
Summary
CHAPTER 7: Supervised Learning—Classification Using Logistic Regression
What Is Logistic Regression?
Using the Breast Cancer Wisconsin (Diagnostic) Data Set
Summary
CHAPTER 8: Supervised Learning—Classification Using Support Vector Machines
What Is a Support Vector Machine?
Kernel Trick
Types of Kernels
Using SVM for Real‐Life Problems
Summary
CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN)
What Is K‐Nearest Neighbors?
Summary
CHAPTER 10: Unsupervised Learning—Clustering Using K‐Means
What Is Unsupervised Learning?
Using K‐Means to Solve Real‐Life Problems
Summary
CHAPTER 11: Using Azure Machine Learning Studio
What Is Microsoft Azure Machine Learning Studio?
Summary
CHAPTER 12: Deploying Machine Learning Models
Deploying ML
Case Study
Deploying the Model
Creating the Client Application to Use the Model
Summary
Index
End User License Agreement
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Introduction
Python
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Machine Learning
Wei-Meng Lee
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