| 000 -LEADER |
|---|
| fixed length control field |
03630nam a22001937a 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| ISBN |
9781800209718 |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
English |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
005.133 |
| Item number |
LIU |
| 100 ## - MAIN ENTRY--AUTHOR NAME |
|---|
| Author name |
Liu, Yuxi (Hayden) |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Python machine learning by example : |
| Sub Title |
build intelligent systems using python, TensorFlow 2, PyTorch, and scikit-learn / |
| Statement of responsibility, etc |
Yuxi (Hayden) Liu |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
3rd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication |
Birmingham, UK : |
| Name of publisher |
Packt Publishing, |
| Year of publication |
2020. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Number of Pages |
xvi, 502 pages : |
| Other physical details |
illustration ; |
| Dimensions |
25 cm. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Cover --<br/>Copyright --<br/>Packt Page --<br/>Contributors --<br/>Table of Contents --<br/>Preface --<br/>Chapter 1: Getting Started with Machine Learning and Python --<br/>An introduction to machine learning --<br/>Understanding why we need machine learning --<br/>Differentiating between machine learning and automation --<br/>Machine learning applications --<br/>Knowing the prerequisites --<br/>Getting started with three types of machine learning --<br/>A brief history of the development of machine learning algorithms --<br/>Digging into the core of machine learning --<br/>Generalizing with data Overfitting, underfitting, and the bias-variance trade-off --<br/>Overfitting --<br/>Underfitting --<br/>The bias-variance trade-off --<br/>Avoiding overfitting with cross-validation --<br/>Avoiding overfitting with regularization --<br/>Avoiding overfitting with feature selection and dimensionality reduction --<br/>Data preprocessing and feature engineering --<br/>Preprocessing and exploration --<br/>Dealing with missing values --<br/>Label encoding --<br/>One-hot encoding --<br/>Scaling --<br/>Feature engineering --<br/>Polynomial transformation --<br/>Power transforms --<br/>Binning --<br/>Combining models --<br/>Voting and averaging --<br/>Bagging --<br/>Boosting Stacking --<br/>Installing software and setting up --<br/>Setting up Python and environments --<br/>Installing the main Python packages --<br/>NumPy --<br/>SciPy --<br/>Pandas --<br/>Scikit-learn --<br/>TensorFlow --<br/>Introducing TensorFlow 2 --<br/>Summary --<br/>Exercises --<br/>Chapter 2: Building a Movie Recommendation Engine with Naïve Bayes --<br/>Getting started with classification --<br/>Binary classification --<br/>Multiclass classification --<br/>Multi-label classification --<br/>Exploring Naïve Bayes --<br/>Learning Bayes' theorem by example --<br/>The mechanics of Naïve Bayes --<br/>Implementing Naïve Bayes --<br/>Implementing Naïve Bayes from scratch Implementing Naïve Bayes with scikit-learn --<br/>Building a movie recommender with Naïve Bayes --<br/>Evaluating classification performance --<br/>Tuning models with cross-validation --<br/>Summary --<br/>Exercise --<br/>References --<br/>Chapter 3: Recognizing Faces with Support Vector Machine --<br/>Finding the separating boundary with SVM --<br/>Scenario 1 --<br/>identifying a separating hyperplane --<br/>Scenario 2 - determining the optimal hyperplane --<br/>Scenario 3 - handling outliers --<br/>Implementing SVM --<br/>Scenario 4 --<br/>dealing with more than two classes --<br/>Scenario 5 --<br/>solving linearly non-separable problems with kernels Choosing between linear and RBF kernels --<br/>Classifying face images with SVM --<br/>Exploring the face image dataset --<br/>Building an SVM-based image classifier --<br/>Boosting image classification performance with PCA --<br/>Fetal state classification on cardiotocography --<br/>Summary --<br/>Exercises --<br/>Chapter 4: Predicting Online Ad Click-Through with Tree-Based Algorithms --<br/>A brief overview of ad click-through prediction --<br/>Getting started with two types of data - numerical and categorical --<br/>Exploring a decision tree from the root to the leaves --<br/>Constructing a decision tree |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Subject |
Python (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Subject |
Machine learning. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Subject |
Programming & scripting languages: |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Books |
