Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch

Table of Contents
About the Authors
About the Technical Reviewer
Acknowledgments
Introduction
Section 1: Python for Machine Learning
	Chapter 1: Getting Started with Python 3 and Jupyter Notebook
		Python 3 Programming Language
			History of Python Programming Language
				Philosophy of Python Programming Language
			Where Python Is Used
		Installing Python
			Python on Linux Distributions
			Python on macOS
		Python Modes
			Interactive Mode
			Script Mode
		Pip3 Utility
		Scientific Python Ecosystem
		Python Implementations and Distributions
		Anaconda
		Summary
	Chapter 2: Getting Started with NumPy
		Getting Started with NumPy
			Multidimensional Ndarrays
		Indexing of Ndarrays
		Ndarray Properties
		NumPy Constants
		Summary
	Chapter 3: Introduction to Data Visualization
		NumPy Routines for Ndarray Creation
		Matplotlib Data Visualization
		Summary
	Chapter 4: Introduction to Pandas
		Pandas Basics
		Series in Pandas
			Properties of Series
		Pandas Dataframes
		Visualizing the Data in Dataframes
		Summary
Section 2: Machine Learning Approaches
	Chapter 5: Introduction to Machine Learning with Scikit-learn
		Learning from Data
			Supervised Learning
				Classification
				Regression
			Unsupervised Learning
		Structure of a Machine Learning System
			Problem Understanding
			Data Collection
			Data Annotation and Data Preparation
			Data Wrangling
			Model Development, Training, and Evaluation
			Model Deployment
		Scikit-Learn
		Installing Scikit-Learn
		Understanding the API
		Your First Scikit-learn Experiment
		Summary
	Chapter 6: Preparing Data for Machine Learning
		Types of Data Variables
			Nominal Data
			Ordinal Data
			Interval Data
			Ratio Data
		Transformation
			Transforming Nominal Attributes
			Transforming Ordinal Attributes
		Normalization
			Min-Max Scaling
			Standard Scaling
		Preprocessing Text
			Preparing NLTK
			Five-Step NLP Pipeline
				1. Segmentation
				2. Tokenization
				Stemming and Lemmatization
				Removing Stopwords
				Preparing Word Vectors
		Preprocessing Images
		Summary
	Chapter 7: Supervised Learning Methods: Part 1
		Linear Regression
			Finding the Regression Line
				Linear Regression Using Python
				Visualizing What We Learned
				Evaluating Linear Regression
		Logistic Regression
			Line vs. Curve for Expression Probability
			Learning the Parameters
			Logistic Regression Using Python
			Visualizing the Decision Boundary
		Decision Trees
			Building a Decision Tree
				Picking the Splitting Attribute
			Decision Tree in Python
				Pruning the Trees
				Interpreting Decision Trees
		Summary
	Chapter 8: Tuning Supervised Learners
		Training and Testing Processes
		Measures of Performance
			Confusion Matrix
				Recall
			Precision
			Accuracy
			F-Measure
			Performance Metrics in Python
				Classification Report
		Cross Validation
			Why Cross Validation?
			Cross Validation in Python
		ROC Curve
		Overfitting and Regularization
			Bias and Variance
			Regularization
				L1 and L2 Regularization
		Hyperparameter Tuning
			Effect of Hyperparameters
				Grid Search
				Random Search
		Summary
	Chapter 9: Supervised Learning Methods: Part 2
		Naive Bayes
			Bayes Theorem
			Conditional Probability
			How Naive Bayes Works
			Multinomial Naive Bayes
			Naive Bayes in Python
		Support Vector Machines
			How SVM Works
			Nonlinear Classification
			Kernel Trick in SVM
			Support Vector Machines in Python
		Summary
	Chapter 10: Ensemble Learning Methods
		Bagging and Random Forest
			Random Forest in Python
		Boosting
			Boosting in Python
		Stacking Ensemble
			Stacking in Python
		Summary
	Chapter 11: Unsupervised Learning Methods
		Dimensionality Reduction
			Understanding the Curse of Dimensionality
			Principal Component Analysis
			Principal Component Analysis in Python
		Clustering
			Clustering Using K-Means
			K-Means in Python
				What Is the Right K?
				Clustering for Image Segmentation
				Clustering Using DBSCAN
		Frequent Pattern Mining
			Market Basket Analysis
			Frequent Pattern Mining in Python
		Summary
Section 3: Neural Networks and Deep Learning
	Chapter 12: Neural Network and PyTorch Basics
		Installing PyTorch
		PyTorch Basics
			Creating a Tensor
			Tensor Operations
		Perceptron
			Perceptron in Python
		Artificial Neural Networks
		Summary
	Chapter 13: Feedforward Neural Networks
		Feedforward Neural Network
			Training Neural Networks
				Gradient Descent
				Backpropagation
			Loss Functions
				Mean Squared Error (MSE)
				Mean Absolute Error
				Negative Log Likelihood Loss
				Cross Entropy Loss
				Hinge Loss
		ANN for Regression
		Activation Functions
			ReLU Activation Function
			Sigmoid Activation Function
			Tanh Activation Function
		Multilayer ANN
			NN Class in PyTorch
			Overfitting and Dropouts
		Classifying Handwritten Digits
		Summary
	Chapter 14: Convolutional Neural Networks
		Convolution Operation
		Structure of a CNN
			Padding and Stride
		CNN in PyTorch
		Image Classification Using CNN
			What Did the Model Learn?
		Deep Networks of CNN
		Summary
	Chapter 15: Recurrent Neural Networks
		Recurrent Unit
		Types of RNN
			One to One
			One to Many
			Many to One
			Many to Many
		RNN in Python
		Long Short-Term Memory
			LSTM Cell
		Time Series Prediction
		Gated Recurrent Unit
		Summary
	Chapter 16: Bringing It All Together
		Data Science Life Cycle
			CRISP-DM Process
				Phase 1: Business Understanding
				Phase 2: Data Understanding
				Phase 3: Data Preparation
				Phase 4: Modelling
				Phase 5: Evaluation
				Phase 6: Deployment
		How ML Applications Are Served
		Learning with an Example
			Defining the Problem
			Data
			Preparing the Model
			Serializing for Future Predictions
			Hosting the Model
				Hello World in Flask
		What’s Next
Index