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Python machine learning by example : build intelligent systems using python, TensorFlow 2, PyTorch, and scikit-learn / Yuxi (Hayden) Liu

By: Liu, Yuxi (Hayden)Material type: TextTextLanguage: English Publisher: Birmingham, UK : Packt Publishing, 2020Edition: 3rd edDescription: xvi, 502 pages : illustration ; 25 cmISBN: 9781800209718Subject(s): Python (Computer program language) | Machine learning | Programming & scripting languagesDDC classification: 005.133
Contents:
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
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Books Institute of Public Enterprise, Library
S Campus 		005.133 LIU (Browse shelf) Checked out 11/20/2023 46210
Books Institute of Public Enterprise, Library
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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

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