Mastering machine learning with scikit-learn: apply effective learning algorithms to real-world problems using scikit-learn
If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential
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| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Olton Birmingham
Packt Publishing
2014
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Summary: | If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | IV, 221 Seiten Illustrationen |
| ISBN: | 9781783988365 |
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| adam_text | Table of Contents
Preface__________________________________________________________________1
Chapter 1: The Fundamentals of Machine Learning__________________________
Learning from experience 8
Machine learning tasks 10
Training data and test data 11
Performance measures, bias, and variance 13
An introduction to scikit-learn 16
Installing scikit-learn 16
Installing scikit-learn on Windows 17
Installing scikit-learn on Linux 17
Installing scikit-learn on OS X 18
Verifying the installation 18
Installing pandas and matplotlib 18
Summary 19
Chapter 2: Linear Regression ___________________________________________21
Simple linear regression 21
Evaluating the fitness of a model with a cost function 25
Solving ordinary least squares for simple linear regression 27
Evaluating the model 29
Multiple linear regression 31
Polynomial regression 35
Regularization 40
Applying linear regression 41
Exploring the data 41
Fitting and evaluating the model 44
Fitting models with gradient descent 46
Summary 50
Table of Contents
Chapter 3: Feature Extraction and Preprocessing___________________________51
Extracting features from categorical variables 51
Extracting features from text 52
The bag-of-words representation 52
Stop-word filtering 55
Stemming and lemmatization 56
Extending bag-of-words with TF-IDF weights 59
Space-efficient feature vectorizing with the hashing trick 62
Extracting features from images 63
Extracting features from pixel intensities 63
Extracting points of interest as features 65
SIFT and SURF 67
Data standardization 69
Summary 70
Chapter 4: From Linear Regression to Logistic Regression__________________71
Binary classification with logistic regression 72
Spam filtering 73
Binary classification performance metrics 76
Accuracy 77
Precision and recall 79
Calculating the F1 measure 80
ROC AUC 81
Tuning models with grid search 84
Multi-class classification 86
Multi-class classification performance metrics 90
Multi-label classification and problem transformation 91
Multi-label classification performance metrics 94
Summary 95
Chapter 5: Nonlinear Classification and Regression with
Decision Trees____________________________________________________________97
Decision trees 97
Training decision trees 99
Selecting the questions 100
Information gain 103
Gini impurity 108
Decision trees with scikit-learn 109
Tree ensembles 112
The advantages and disadvantages of decision trees 113
Summary 114
Table of Contents
Chapter 6: Clustering with K-Means____________________________________115
Clustering with the K-Means algorithm 117
Local optima 123
The elbow method 124
Evaluating clusters 128
Image quantization 130
Clustering to learn features 132
Summary 135
Chapter 7: Dimensionality Reduction with PCA__________________________137
An overview of PCA 137
Performing Principal Component Analysis 142
Variance, Covariance, and Covariance Matrices 142
Eigenvectors and eigenvalues 143
Dimensionality reduction with Principal Component Analysis 146
Using PCA to visualize high-dimensional data 149
Face recognition with PCA 150
Summary 153
Chapter 8: The Perceptron ___________________________________________ 155
Activation functions 157
The perceptron learning algorithm 158
Binary classification with the perceptron 159
Document classification with the perceptron 166
Limitations of the perceptron 167
Summary 169
Chapter 9: From the Perceptron to Support Vector Machines 171
Kernels and the kernel trick 172
Maximum margin classification and support vectors 176
Classifying characters in scikit-learn 179
Classifying handwritten digits 179
Classifying characters in natural images 182
Summary 185
Chapter 10: From the Perceptron to Artificial Neural Networks 187
Nonlinear decision boundaries 188
Feedforward and feedback artificial neural networks 189
Multilayer perceptrons 189
Minimizing the cost function 191
Forward propagation 102
Backpropagation 198
Table of Contents
Approximating XOR with Multilayer perceptrons 212
Classifying handwritten digits 213
Summary 214
Index 217
IV
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| spelling | Hackeling, Gavin Verfasser aut Mastering machine learning with scikit-learn apply effective learning algorithms to real-world problems using scikit-learn Gavin Hackeling Olton Birmingham Packt Publishing 2014 IV, 221 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Description based on publisher supplied metadata and other sources If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential Open source software Python (Computer program language) Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Erscheint auch als Online-Ausgabe 978-1-78398-837-2 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029048397&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hackeling, Gavin Mastering machine learning with scikit-learn apply effective learning algorithms to real-world problems using scikit-learn Open source software Python (Computer program language) Maschinelles Lernen (DE-588)4193754-5 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
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| title | Mastering machine learning with scikit-learn apply effective learning algorithms to real-world problems using scikit-learn |
| title_auth | Mastering machine learning with scikit-learn apply effective learning algorithms to real-world problems using scikit-learn |
| title_exact_search | Mastering machine learning with scikit-learn apply effective learning algorithms to real-world problems using scikit-learn |
| title_full | Mastering machine learning with scikit-learn apply effective learning algorithms to real-world problems using scikit-learn Gavin Hackeling |
| title_fullStr | Mastering machine learning with scikit-learn apply effective learning algorithms to real-world problems using scikit-learn Gavin Hackeling |
| title_full_unstemmed | Mastering machine learning with scikit-learn apply effective learning algorithms to real-world problems using scikit-learn Gavin Hackeling |
| title_short | Mastering machine learning with scikit-learn |
| title_sort | mastering machine learning with scikit learn apply effective learning algorithms to real world problems using scikit learn |
| title_sub | apply effective learning algorithms to real-world problems using scikit-learn |
| topic | Open source software Python (Computer program language) Maschinelles Lernen (DE-588)4193754-5 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| topic_facet | Open source software Python (Computer program language) Maschinelles Lernen Python Programmiersprache |
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