Artificial Intelligence with Python. Machine Learning: Foundations, Methodologies, and Applications

Preface
Acknowledgments
Contents
Part I Python
	1 Python for Artificial Intelligence
		1.1 Common Uses
			1.1.1 Relative Popularity
			1.1.2 Features
			1.1.3 Syntax and Design
		1.2 Scientific Programming
		1.3 Why Python for Artificial Intelligence
	2 Getting Started
		2.1 Setting up Your Python Environment
		2.2 Anaconda
			2.2.1 Installing Anaconda
			2.2.2 Further Installation Steps
			2.2.3 Updating Anaconda
		2.3 Installing Packages
		2.4 Virtual Environment
		2.5 Jupyter Notebooks
			2.5.1 Starting the Jupyter Notebook
			2.5.2 Notebook Basics
				Running Cells
				Modal Editing
				Inserting Unicode (e.g., Greek Letters)
				A Test Program
			2.5.3 Working with the Notebook
				Tab Completion
				On-Line Help
				Other Content
			2.5.4 Sharing Notebooks
	3 An Introductory Example
		3.1 Overview
		3.2 The Task: Plotting a White Noise Process
		3.3 Our First Program
			3.3.1 Imports
				Why So Many Imports?
				Packages
				Subpackages
			3.3.2 Importing Names Directly
			3.3.3 Random Draws
		3.4 Alternative Implementations
			3.4.1 A Version with a for Loop
			3.4.2 Lists
			3.4.3 The for Loop
			3.4.4 A Comment on Indentation
			3.4.5 While Loops
		3.5 Another Application
		3.6 Exercises
			3.6.1 Exercise 1
			3.6.2 Exercise 2
			3.6.3 Exercise 3
			3.6.4 Exercise 4
			3.6.5 Exercise 5
		3.7 Solutions
			3.7.1 Exercise 1
			3.7.2 Exercise 2
			3.7.3 Exercise 3
			3.7.4 Exercise 4
			3.7.5 Exercise 5
	4 Basic Python
		4.1 Hello, World!
		4.2 Indentation
		4.3 Variables and Types
			4.3.1 Numbers
			4.3.2 Strings
			4.3.3 Lists
			4.3.4 Dictionaries
		4.4 Basic Operators
			4.4.1 Arithmetic Operators
			4.4.2 List Operators
			4.4.3 String Operators
		4.5 Logical Conditions
		4.6 Loops
		4.7 List Comprehensions
		4.8 Exception Handling
			4.8.1 Sets
	5 Intermediate Python
		5.1 Functions
		5.2 Classes and Objects
		5.3 Modules and Packages
			5.3.1 Writing Modules
		5.4 Built-in Modules
		5.5 Writing Packages
		5.6 Closures
		5.7 Decorators
	6 Advanced Python
		6.1 Python Magic Methods
			6.1.1 Exercise
			6.1.2 Solution
		6.2 Comprehension
		6.3 Functional Parts
		6.4 Iterables
		6.5 Decorators
		6.6 More on Object Oriented Programming
			6.6.1 Mixins
			6.6.2 Attribute Access Hooks
			6.6.3 Callable Objects
			6.6.4 _new_ vs _init_
		6.7 Properties
		6.8 Metaclasses
	7 Python for Data Analysis
		7.1 Ethics
		7.2 Data Analysis
			7.2.1 Numpy Arrays
			7.2.2 Pandas
				Selections
			7.2.3 Matplotlib
		7.3 Sample Code
Part II Artificial Intelligence Basics
	8 Introduction to Artificial Intelligence
		8.1 Data Exploration
		8.2 Problems with Data
		8.3 A Language and Approach to Data-Driven Story-Telling
		8.4 Example: Telling Story with Data
	9 Data Wrangling
		9.1 Handling Missing Data
			9.1.1 Missing Data
			9.1.2 Removing Missing Data
		9.2 Transformation
			9.2.1 Duplicates
			9.2.2 Mapping
		9.3 Outliers
		9.4 Permutation
		9.5 Merging and Combining
		9.6 Reshaping and Pivoting
		9.7 Wide to Long
	10 Regression
		10.1 Linear Regression
		10.2 Decision Tree Regression
		10.3 Random Forests
		10.4 Neural Network
		10.5 How to Improve Our Regression Model
			10.5.1 Boxplot
			10.5.2 Remove Outlier
			10.5.3 Remove NA
		10.6 Feature Importance
		10.7 Sample Code
	11 Classification
		11.1 Logistic Regression
		11.2 Decision Tree and Random Forest
		11.3 Neural Network
		11.4 Logistic Regression
		11.5 Decision Tree
		11.6 Feature Importance
		11.7 Remove Outlier
		11.8 Use Top 3 Features
		11.9 SVM
			11.9.1 Important Hyper Parameters
		11.10 Naive Bayes
		11.11 Sample Code
	12 Clustering
		12.1 What Is Clustering?
		12.2 K-Means
		12.3 The Elbow Method
	13 Association Rules
		13.1 What Are Association Rules
		13.2 Apriori Algorithm
		13.3 Measures for Association Rules
Part III Artificial Intelligence Implementations
	14 Text Mining
		14.1 Read Data
		14.2 Date Range
		14.3 Category Distribution
		14.4 Texts for Classification
		14.5 Vectorize
		14.6 CountVectorizer
		14.7 TF-IDF
		14.8 Feature Extraction with TF-IDF
		14.9 Sample Code
	15 Image Processing
		15.1 Load the Dependencies
		15.2 Load Image from urls
		15.3 Image Analysis
		15.4 Image Histogram
		15.5 Contour
		15.6 Grayscale Transformation
		15.7 Histogram Equalization
		15.8 Fourier Transformation
		15.9 High pass Filtering in FFT
		15.10 Pattern Recognition
		15.11 Sample Code
	16 Convolutional Neural Networks
		16.1 The Convolution Operation
		16.2 Pooling
		16.3 Flattening
		16.4 Exercise
		16.5 CNN Architectures
			16.5.1 VGG16
			16.5.2 Inception Net
			16.5.3 ResNet
		16.6 Finetuning
		16.7 Other Tasks That Use CNNs
			16.7.1 Object Detection
			16.7.2 Semantic Segmentation
	17 Chatbot, Speech, and NLP
		17.1 Speech to Text
		17.2 Importing the Packages for Chatbot
		17.3 Preprocessing the Data for Chatbot
			17.3.1 Download the Data
			17.3.2 Reading the Data from the Files
			17.3.3 Preparing Data for Seq2Seq Model
		17.4 Defining the Encoder-Decoder Model
		17.5 Training the Model
		17.6 Defining Inference Models
		17.7 Talking with Our Chatbot
		17.8 Sample Code
	18 Deep Convolutional Generative Adversarial Network
		18.1 What Are GANs?
		18.2 Setup
			18.2.1 Load and Prepare the Dataset
		18.3 Create the Models
			18.3.1 The Generator
			18.3.2 The Discriminator
		18.4 Define the Loss and Optimizers
			18.4.1 Discriminator Loss
			18.4.2 Generator Loss
		18.5 Save Checkpoints
		18.6 Define the Training Loop
			18.6.1 Train the Model
			18.6.2 Create a GIF
	19 Neural Style Transfer
		19.1 Setup
			19.1.1 Import and Configure Modules
		19.2 Visualize the Input
		19.3 Fast Style Transfer Using TF-Hub
		19.4 Define Content and Style Representations
			19.4.1 Intermediate Layers for Style and Content
		19.5 Build the Model
		19.6 Calculate Style
		19.7 Extract Style and Content
		19.8 Run Gradient Descent
		19.9 Total Variation Loss
		19.10 Re-run the Optimization
	20 Reinforcement Learning
		20.1 Reinforcement Learning Analogy
		20.2 Q-learning
		20.3 Running a Trained Taxi
Bibliography
Index