Cover --
Copyright --
Packt Page --
Contributors --
Table of Contents --
Preface --
Chapter 1: Getting Started with Machine Learning and Python --
An introduction to machine learning --
Understanding why we need machine learning --
Differentiating between machine learning and automation --
Machine learning applications --
Knowing the prerequisites --
Getting started with three types of machine learning --
A brief history of the development of machine learning algorithms --
Digging into the core of machine learning --
Generalizing with data Overfitting, underfitting, and the bias-variance trade-off --
Overfitting --
Underfitting --
The bias-variance trade-off --
Avoiding overfitting with cross-validation --
Avoiding overfitting with regularization --
Avoiding overfitting with feature selection and dimensionality reduction --
Data preprocessing and feature engineering --
Preprocessing and exploration --
Dealing with missing values --
Label encoding --
One-hot encoding --
Scaling --
Feature engineering --
Polynomial transformation --
Power transforms --
Binning --
Combining models --
Voting and averaging --
Bagging --
Boosting Stacking --
Installing software and setting up --
Setting up Python and environments --
Installing the main Python packages --
NumPy --
SciPy --
Pandas --
Scikit-learn --
TensorFlow --
Introducing TensorFlow 2 --
Summary --
Exercises --
Chapter 2: Building a Movie Recommendation Engine with Naïve Bayes --
Getting started with classification --
Binary classification --
Multiclass classification --
Multi-label classification --
Exploring Naïve Bayes --
Learning Bayes' theorem by example --
The mechanics of Naïve Bayes --
Implementing Naïve Bayes --
Implementing Naïve Bayes from scratch Implementing Naïve Bayes with scikit-learn --
Building a movie recommender with Naïve Bayes --
Evaluating classification performance --
Tuning models with cross-validation --
Summary --
Exercise --
References --
Chapter 3: Recognizing Faces with Support Vector Machine --
Finding the separating boundary with SVM --
Scenario 1 --
identifying a separating hyperplane --
Scenario 2 - determining the optimal hyperplane --
Scenario 3 - handling outliers --
Implementing SVM --
Scenario 4 --
dealing with more than two classes --
Scenario 5 --
solving linearly non-separable problems with kernels Choosing between linear and RBF kernels --
Classifying face images with SVM --
Exploring the face image dataset --
Building an SVM-based image classifier --
Boosting image classification performance with PCA --
Fetal state classification on cardiotocography --
Summary --
Exercises --
Chapter 4: Predicting Online Ad Click-Through with Tree-Based Algorithms --
A brief overview of ad click-through prediction --
Getting started with two types of data - numerical and categorical --
Exploring a decision tree from the root to the leaves --
Constructing a decision tree