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by Andrea Isoni
Machine Learning for the Web
Machine Learning for the Web
Table of Contents
Machine Learning for the Web
Credits
Foreword
About the Author
About the Reviewers
www.PacktPub.com
eBooks, discount offers, and more
Why subscribe?
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Introduction to Practical Machine Learning Using Python
General machine-learning concepts
Machine-learning example
Installing and importing a module (library)
Preparing, manipulating and visualizing data – NumPy, pandas and matplotlib tutorials
Using NumPy
Arrays creation
Array manipulations
Array operations
Linear algebra operations
Statistics and mathematical functions
Understanding the pandas module
Exploring data
Manipulate data
Matplotlib tutorial
Scientific libraries used in the book
When to use machine learning
Summary
2. Unsupervised Machine Learning
Clustering algorithms
Distribution methods
Expectation maximization
Mixture of Gaussians
Centroid methods
k-means
Density methods
Mean – shift
Hierarchical methods
Training and comparison of the clustering methods
Dimensionality reduction
Principal Component Analysis (PCA)
PCA example
Singular value decomposition
Summary
3. Supervised Machine Learning
Model error estimation
Generalized linear models
Linear regression
Ridge regression
Lasso regression
Logistic regression
Probabilistic interpretation of generalized linear models
k-nearest neighbours (KNN)
Naive Bayes
Multinomial Naive Bayes
Gaussian Naive Bayes
Decision trees
Support vector machine
Kernel trick
A comparison of methods
Regression problem
Classification problem
Hidden Markov model
A Python example
Summary
4. Web Mining Techniques
Web structure mining
Web crawlers (or spiders)
Indexer
Ranking – PageRank algorithm
Web content mining
Parsing
Natural language processing
Information retrieval models
TF-IDF
Latent Semantic Analysis (LSA)
Doc2Vec (word2vec)
Word2vec – continuous bag of words and skip-gram architectures
Mathematical description of the CBOW model
Doc2Vec extension
Movie review query example
Postprocessing information
Latent Dirichlet allocation
Model
Example
Opinion mining (sentiment analysis)
Summary
5. Recommendation Systems
Utility matrix
Similarities measures
Collaborative Filtering methods
Memory-based Collaborative Filtering
User-based Collaborative Filtering
Item-based Collaborative Filtering
Simplest item-based Collaborative Filtering – slope one
Model-based Collaborative Filtering
Alternative least square (ALS)
Stochastic gradient descent (SGD)
Non-negative matrix factorization (NMF)
Singular value decomposition (SVD)
CBF methods
Item features average method
Regularized linear regression method
Association rules for learning recommendation system
Log-likelihood ratios recommendation system method
Hybrid recommendation systems
Evaluation of the recommendation systems
Root mean square error (RMSE) evaluation
Classification metrics
Summary
6. Getting Started with Django
HTTP – the basics of the GET and POST methods
Installation and server creation
Settings
Writing an app – most important features
Models
URL and views behind HTML web pages
HTML pages
URL declarations and views
Admin
Shell interface
Commands
RESTful application programming interfaces (APIs)
Summary
7. Movie Recommendation System Web Application
Application setup
Models
Commands
User sign up login/logout implementation
Information retrieval system (movies query)
Rating system
Recommendation systems
Admin interface and API
Summary
8. Sentiment Analyser Application for Movie Reviews
Application usage overview
Search engine choice and the application code
Scrapy setup and the application code
Scrapy settings
Scraper
Pipelines
Crawler
Django models
Integrating Django with Scrapy
Commands (sentiment analysis model and delete queries)
Sentiment analysis model loader
Deleting an already performed query
Sentiment reviews analyser – Django views and HTML
PageRank: Django view and the algorithm code
Admin and API
Summary
Index
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Prev
Previous Chapter
Summary
Index
A
ad.data file
URL /
Exploring data
admin
creating /
Admin
shell interface, creating /
Shell interface
commands, writing /
Commands
RESTful API /
RESTful application programming interfaces (APIs)
API, implementing /
Admin and API
agglomeration, linkage criteria
single linkage /
Hierarchical methods
complete linkage /
Hierarchical methods
UPGMA /
Hierarchical methods
average linkage /
Hierarchical methods
Ward algorithm /
Hierarchical methods
agglomerative clustering
about /
Hierarchical methods
Alternating least Square (ALS)
about /
Alternative least square (ALS)
API
implementing, for admin /
Admin and API
app
writing /
Writing an app – most important features
models, creating /
Models
URL /
URL and views behind HTML web pages
HTML web pages /
URL and views behind HTML web pages
URL declarations /
URL declarations and views
views /
URL declarations and views
array manipulations
unique method /
Array manipulations
random method /
Array manipulations
shuffle method /
Array manipulations
sort method /
Array manipulations
argsort method /
Array manipulations
array_equal method /
Array manipulations
flatten method /
Array manipulations
transpose method /
Array manipulations
reshape method /
Array manipulations
concatenate method /
Array manipulations
fromstring method /
Array manipulations
tostring method /
Array manipulations
array operations
take method /
Array operations
put method /
Array operations
arrays
creating /
Arrays creation
arrays creation
Tolist method /
Arrays creation
Copy method /
Arrays creation
ones method /
Arrays creation
zeros method /
Arrays creation
zeros_like, ones_like method /
Arrays creation
Fill method /
Arrays creation
identity method /
Arrays creation
Eye method /
Arrays creation
vstack method /
Arrays creation
random submodule method /
Arrays creation
association rules learning recommendation system
about /
Association rules for learning recommendation system
support /
Association rules for learning recommendation system
confidence /
Association rules for learning recommendation system
B
batch gradient descent
about /
Generalized linear models
Baum-Welch algorithm
about /
Hidden Markov model
BeautifulSoup /
Movie review query example
Bostons housing dataset
URL /
Regression problem
breadth-first algorithm
about /
Web crawlers (or spiders)
C
centroid methods
about /
Centroid methods
k-means /
k-means
classification
about /
General machine-learning concepts
methods, comparing /
A comparison of methods
classification problem
solving /
Classification problem
clustering algorithms
about /
Clustering algorithms
distribution methods /
Distribution methods
centroid methods /
Centroid methods
density methods /
Density methods
hierarchical methods /
Hierarchical methods
clustering methods
training /
Training and comparison of the clustering methods
comparison /
Training and comparison of the clustering methods
cold start
about /
Collaborative Filtering methods
Collaborative Filtering methods
about /
Collaborative Filtering methods
memory-based collaborative filtering /
Memory-based Collaborative Filtering
model-based Collaborative Filtering /
Model-based Collaborative Filtering
commands
creating /
Commands
concentration parameter /
Model
Content-based Filtering (CBF) methods
about /
CBF methods
item features average method /
Item features average method
regularized linear regression method /
Regularized linear regression method
Continuous Bag Of Words (CBOW)
about /
Word2vec – continuous bag of words and skip-gram architectures
mathematical description /
Mathematical description of the CBOW model
cosine similarity
about /
User-based Collaborative Filtering
Crawlera
URL /
Search engine choice and the application code
cross-site forgery protection
reference link /
HTML pages
Cython /
Scientific libraries used in the book
D
damping factor
about /
Ranking – PageRank algorithm
,
PageRank: Django view and the algorithm code
data
preparing /
Preparing, manipulating and visualizing data – NumPy, pandas and matplotlib tutorials
manipulating /
Preparing, manipulating and visualizing data – NumPy, pandas and matplotlib tutorials
visualizing /
Preparing, manipulating and visualizing data – NumPy, pandas and matplotlib tutorials
DataFrame
about /
Machine-learning example
,
Exploring data
DBSCAN /
Scientific libraries used in the book
about /
Density methods
decision trees
about /
Decision trees
Decision Trees /
Scientific libraries used in the book
delete_query command
using /
Deleting an already performed query
dendrogram
about /
Hierarchical methods
density methods
about /
Density methods
mean - shift /
Mean – shift
dimensionality reduction
about /
Dimensionality reduction
Principal Component Analysis (PCA) /
Principal Component Analysis (PCA)
distributed bag of words (DBOW) /
Doc2Vec extension
distributed memory model (DM) /
Doc2Vec extension
distribution methods
about /
Distribution methods
expectation maximization /
Expectation maximization
mixture of Gaussian /
Mixture of Gaussians
divisive clustering
about /
Hierarchical methods
Django
about /
General machine-learning concepts
,
Scientific libraries used in the book
installation /
Installation and server creation
server, creating /
Installation and server creation
settings.py file /
Settings
models /
Django models
integrating, with Scrapy /
Integrating Django with Scrapy
Django integration
sentiment analysis model, defining /
Commands (sentiment analysis model and delete queries)
queries, deleting /
Commands (sentiment analysis model and delete queries)
sentiment classifier, building /
Sentiment analysis model loader
delete_query command, using /
Deleting an already performed query
reviews, calculating /
Sentiment reviews analyser – Django views and HTML
Doc2Vec
about /
Doc2Vec (word2vec)
extension /
Doc2Vec extension
E
Eigenfaces
URL /
Principal Component Analysis (PCA)
Euclidean norm
about /
Centroid methods
expectation maximization
about /
Expectation maximization
E-step /
Expectation maximization
log-likelihood function /
Expectation maximization
M-step /
Expectation maximization
F
features
about /
General machine-learning concepts
frontier
about /
Web crawlers (or spiders)
fuzzy c-means
about /
Centroid methods
G
Gaussian Naive Bayes
about /
Naive Bayes
,
Gaussian Naive Bayes
Gaussians clustering
about /
Distribution methods
generalized linear models
about /
Generalized linear models
linear regression /
Linear regression
ridge regression /
Ridge regression
Lasso regression /
Lasso regression
logistic regression /
Logistic regression
probabilistic interpretation /
Probabilistic interpretation of generalized linear models
k-nearest neighbours (KNN) /
k-nearest neighbours (KNN)
GET method
about /
HTTP – the basics of the GET and POST methods
H
Hidden Markov model
Python example /
A Python example
hidden Markov model (HMM)
about /
Hidden Markov model
hierarchical methods
about /
Hierarchical methods
HTML web pages
about /
URL and views behind HTML web pages
creating /
HTML pages
hybrid recommendation systems
about /
Hybrid recommendation systems
weighted /
Hybrid recommendation systems
mixed /
Hybrid recommendation systems
switched /
Hybrid recommendation systems
feature combination /
Hybrid recommendation systems
feature augmentation /
Hybrid recommendation systems
Hypertext Transfer Protocol (HTTP)
about /
HTTP – the basics of the GET and POST methods
POST method /
HTTP – the basics of the GET and POST methods
GET method /
HTTP – the basics of the GET and POST methods
I
indexer
about /
Indexer
information retrieval models
about /
Information retrieval models
TF-IDF /
TF-IDF
Latent Semantic Analysis (LSA) /
Latent Semantic Analysis (LSA)
Doc2Vec /
Doc2Vec (word2vec)
Word2vec /
Word2vec – continuous bag of words and skip-gram architectures
inverted index scheme
about /
Indexer
K
k-means /
Scientific libraries used in the book
about /
k-means
k-means (Lloyd's algorithm)
about /
Centroid methods
k-means++
about /
Centroid methods
k-medians clustering
about /
Centroid methods
k-nearest neighbours (KNN)
about /
k-nearest neighbours (KNN)
kernel function
using /
Kernel trick
L
Laplace smoothing
about /
Naive Bayes
Lasso regression
about /
Lasso regression
Latent Dirichlet allocation (LDA)
about /
Latent Dirichlet allocation
model /
Model
example /
Example
latent semantic analysis (LSA)
about /
Information retrieval models
Latent Semantic Analysis (LSA)
about /
Latent Semantic Analysis (LSA)
LIBLINEAR /
Scientific libraries used in the book
libraries
SciPy /
Scientific libraries used in the book
scikit-learn (sklearn) /
Scientific libraries used in the book
Natural Language Toolkit (NLTK) /
Scientific libraries used in the book
Scrapy /
Scientific libraries used in the book
Django /
Scientific libraries used in the book
LIBSVM /
Scientific libraries used in the book
linear algebra operations
dot method /
Linear algebra operations
inner method /
Linear algebra operations
linalg method /
Linear algebra operations
linear regression
about /
Linear regression
log-likelihood ratio /
Commands
log-likelihood ratio (LLR)
about /
Log-likelihood ratios recommendation system method
log-likelihood ratios recommendation system method
about /
Log-likelihood ratios recommendation system method
logistic regression
about /
Logistic regression
M
machine-learning
concepts /
General machine-learning concepts
about /
General machine-learning concepts
example /
Machine-learning example
module (library), importing /
Installing and importing a module (library)
module (library), installing /
Installing and importing a module (library)
machine learning
usage /
When to use machine learning
mathematical functions
Mean /
Statistics and mathematical functions
std /
Statistics and mathematical functions
var /
Statistics and mathematical functions
min /
Statistics and mathematical functions
argmin /
Statistics and mathematical functions
argmax /
Statistics and mathematical functions
MATLAB
about /
Matplotlib tutorial
matplotlib
about /
Matplotlib tutorial
URL /
Matplotlib tutorial
mean - shift
about /
Mean – shift
mean square error (MSE)
about /
Model error estimation
memory-based collaborative filtering
about /
Memory-based Collaborative Filtering
user based collaborative filtering /
User-based Collaborative Filtering
item based collaborative filtering /
Item-based Collaborative Filtering
simplest item-based collaborative filtering /
Simplest item-based Collaborative Filtering – slope one
mixture of Gaussians
about /
Mixture of Gaussians
model-based Collaborative Filtering
Alternative least square (ALS) /
Alternative least square (ALS)
Stochastic gradient descent (SGD) /
Stochastic gradient descent (SGD)
non-negative matrix factorization (NMF) /
Non-negative matrix factorization (NMF)
Singular value decomposition (SVD) /
Singular value decomposition (SVD)
model error estimation
about /
Model error estimation
models
creating /
Models
,
Django models
module (library)
installing /
Installing and importing a module (library)
importing /
Installing and importing a module (library)
movie recommendation system
application setup /
Application setup
models /
Models
commands /
Commands
user sign up login/logout implementation /
User sign up login/logout implementation
information retrieval system (movies query) /
Information retrieval system (movies query)
rating system /
Rating system
recommendation systems /
Recommendation systems
admin interface /
Admin interface and API
API /
Admin interface and API
movie review query example /
Movie review query example
movie sentiment analyzer
usage overview /
Application usage overview
Multinomial Naive Bayes
about /
Naive Bayes
,
Multinomial Naive Bayes
Mutual Information (MI)
about /
Log-likelihood ratios recommendation system method
MySQL /
Settings
N
Naive Bayes /
Scientific libraries used in the book
about /
Naive Bayes
Multinomial Naive Bayes /
Multinomial Naive Bayes
Gaussian Naive Bayes /
Gaussian Naive Bayes
natural language processing
about /
Natural language processing
Natural Language Processing (NLP) /
Scientific libraries used in the book
Natural Language Toolkit (NLTK)
about /
Scientific libraries used in the book
,
Sentiment analysis model loader
non-negative matrix factorization (NMF)
about /
Non-negative matrix factorization (NMF)
Not a Number (NaN)
about /
Exploring data
NumPy
using /
Using NumPy
arrays, creating /
Arrays creation
arrays, manipulating /
Array manipulations
array operations, performing /
Array operations
linear algebra operations, performing /
Linear algebra operations
mathematical functions /
Statistics and mathematical functions
statistics /
Statistics and mathematical functions
O
Octave
about /
Scientific libraries used in the book
opinion mining (sentiment analysis)
about /
Opinion mining (sentiment analysis)
Oracle /
Settings
P
PageRank
about /
PageRank: Django view and the algorithm code
implementing /
PageRank: Django view and the algorithm code
PageRank algorithm
about /
Ranking – PageRank algorithm
pandas module
about /
Understanding the pandas module
data, exploring /
Exploring data
data, manipulating /
Manipulate data
parsing
about /
Parsing
Pearson correlation
about /
User-based Collaborative Filtering
periodic
about /
Ranking – PageRank algorithm
PostgreSQL /
Settings
POST method
about /
HTTP – the basics of the GET and POST methods
postprocessing information
about /
Postprocessing information
Latent Dirichlet allocation (LDA) /
Latent Dirichlet allocation
opinion mining (sentiment analysis) /
Opinion mining (sentiment analysis)
Principal Component Analysis (PCA)
about /
Dimensionality reduction
,
Principal Component Analysis (PCA)
example /
PCA example
probabilistic interpretation
of generalized linear models /
Probabilistic interpretation of generalized linear models
Python example
about /
A Python example
R
random forest
about /
Decision trees
Random Forests /
Scientific libraries used in the book
ranking algorithm
about /
Ranking – PageRank algorithm
rank sink
about /
Ranking – PageRank algorithm
rbf kernel
about /
Regression problem
recommendation systems
evaluation /
Evaluation of the recommendation systems
recommendation systems, evaluation
root mean square error (RMSE) /
Root mean square error (RMSE) evaluation
classification metrics /
Classification metrics
regression
about /
General machine-learning concepts
methods, comparing /
A comparison of methods
regression problem
solving /
Regression problem
RESTful application programming interfaces (APIs)
about /
RESTful application programming interfaces (APIs)
using /
RESTful application programming interfaces (APIs)
ridge regression
about /
Ridge regression
RLab
about /
Scientific libraries used in the book
S
scikit-learn (sklearn)
about /
Scientific libraries used in the book
SciPy
about /
Scientific libraries used in the book
Scrapy
about /
Scientific libraries used in the book
setting up /
Scrapy setup and the application code
settings.py file /
Scrapy settings
scraper /
Scraper
pipelines /
Pipelines
crawler /
Crawler
Django, integrating /
Integrating Django with Scrapy
search engine
selecting /
Search engine choice and the application code
shell interface
creating /
Shell interface
silhouette
about /
Training and comparison of the clustering methods
similarities measures
about /
Similarities measures
similarities measuring
Cosine similarity /
Similarities measures
Pearson correlation /
Similarities measures
singular value decomposition
about /
Singular value decomposition
Singular value decomposition (SVD)
about /
Singular value decomposition (SVD)
Singular Value Decomposition (SVD)
about /
Model-based Collaborative Filtering
Skip-gram
about /
Word2vec – continuous bag of words and skip-gram architectures
softmax formula
about /
Mathematical description of the CBOW model
SQLite3 /
Settings
stemming
about /
Natural language processing
stochastic gradient descent
about /
Generalized linear models
Stochastic gradient descent (SGD)
about /
Stochastic gradient descent (SGD)
supervised learning
about /
General machine-learning concepts
classification /
General machine-learning concepts
regression /
General machine-learning concepts
Support Vector Machine (SVM)
about /
Machine-learning example
,
Support vector machine
kernel function, using /
Kernel trick
T
Term Frequency, Inverse Document Frequency (tf-idf) model /
Commands
TF-IDF
about /
TF-IDF
Tikhonov regularization
about /
Ridge regression
training dataset
about /
General machine-learning concepts
U
unsupervised learning
about /
General machine-learning concepts
URL
declarations /
URL declarations and views
utility matrix
about /
Utility matrix
V
Viterbi algorithm
about /
Hidden Markov model
Voronoi diagram
about /
k-means
W
web content mining
about /
Web content mining
parsing /
Parsing
web crawlers
about /
Web crawlers (or spiders)
web structure mining
about /
Web structure mining
web crawlers /
Web crawlers (or spiders)
indexer /
Indexer
ranking /
Ranking – PageRank algorithm
Word2vec
about /
Word2vec – continuous bag of words and skip-gram architectures
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