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