Index

A

• AcceptedAnswerId attribute / Preselection and processing of attributes
• add-one smoothing / Accounting for unseen words and other oddities
• additive smoothing / Accounting for unseen words and other oddities
• advanced baskets analysis
  • about / More advanced basket analysis
• Amazon Linux
  • Python packages, installing on / Installing Python packages on Amazon Linux
• Amazon regions / Using Amazon Web Services (AWS)
• Apriori algorithm / Analyzing supermarket shopping baskets
• area under curve (AUC) / Looking behind accuracy – precision and recall, An alternate way to measure classifier performance using receiver operator characteristic (ROC)
• as keypoint detection / Local feature representations
• Associated Press (AP)
  • about / Building a topic model
• association rule mining
  • about / Association rule mining
• association rules
  • about / Association rule mining
• attributes
  • preselecting / Preselection and processing of attributes
  • processing / Preselection and processing of attributes
• Auditory Filterbank Temporal Envelope (AFTE) / Improving classification performance with Mel Frequency Cepstral Coefficients
• Automatic Music Genre Classification (AMGC) / Improving classification performance with Mel Frequency Cepstral Coefficients
• AWS
  • using / Using Amazon Web Services (AWS)
  • machine, creating / Creating your first machines

B

• bag-of-word approach
  • about / How to do it, Local feature representations
  • challenges / Preprocessing – similarity measured as similar number of common words
• bag-of-word approach, challenges
  • about / Preprocessing – similarity measured as similar number of common words
  • raw text, converting into bag-of-words / Converting raw text into a bag-of-words
  • words, counting / Counting words
  • word count vectors, normalizing / Normalizing the word count vectors
  • less important words, removing / Removing less important words
  • stemming / Stemming
  • stop words, on steroids / Stop words on steroids
• basic image processing
  • about / Basic image processing
  • thresholding / Thresholding
  • Gaussian blurring / Gaussian blurring
  • filtering, for different effects / Filtering for different effects
• basket analysis
  • about / Basket analysis
  • useful predictions, obtaining / Obtaining useful predictions
  • supermarket shopping baskets, analyzing / Analyzing supermarket shopping baskets
  • association rule mining / Association rule mining
• beer and diapers story / Basket analysis
• BernoulliNB / Creating our first classifier and tuning it
• Bias-variance
  • about / Bias-variance and its trade-off
  • trade-off / Bias-variance and its trade-off
• big data expression
  • about / Learning about big data
• binary classification
  • about / Binary and multiclass classification
• binary matrix of recommendations
  • using / Using the binary matrix of recommendations
• blogs, machine language / Blogs
• Body attribute / Preselection and processing of attributes

C

• classification
  • about / The Iris dataset
  • Naive Bayes, using for / Using Naive Bayes to classify
• classification model
  • building / Building our first classification model
  • evaluating / Evaluation – holding out data and cross-validation
  • structure / Building more complex classifiers
  • search procedure / Building more complex classifiers
  • loss function / Building more complex classifiers
• classification performance
  • improving, with Mel Frequency Cepstral Coefficients / Improving classification performance with Mel Frequency Cepstral Coefficients
• classifier
  • creating / Creating our first classifier, Solving an easy problem first
  • training / Training the classifier, Training the classifier
  • performance, measuring / Measuring the classifier's performance
  • performance, improving / Deciding how to improve
  • slimming / Slimming the classifier
  • integrating, into site / Ship it!
  • classes, using / Using all the classes
  • parameters, tuning / Tuning the classifier's parameters
  • building, FFT used / Using FFT to build our first classifier, Increasing experimentation agility
• classifier, classy answers
  • tuning / Tuning the classifier
• classifier performance
  • measuring, receiver operator characteristic (ROC) used / An alternate way to measure classifier performance using receiver operator characteristic (ROC)
• classifier performance, improving
  • Bias-variance / Bias-variance and its trade-off
  • high bias, fixing / Fixing high bias
  • high variance, fixing / Fixing high variance
  • high bias or low bias / High bias or low bias
• classy answers
  • classifying / Learning to classify classy answers
  • instance, tuning / Tuning the instance
  • classifier, tuning / Tuning the classifier
• cloud machine
  • jug, running on / Running jug on our cloud machine
• cluster generation
  • automating, with starcluster / Automating the generation of clusters with starcluster
• clustering
  • about / Measuring the relatedness of posts, Clustering
  • flat clustering / Clustering
  • hierarchical clustering / Clustering
  • KMeans algorithm / KMeans
  • test data, obtaining for idea evaluation / Getting test data to evaluate our ideas on
• cluster package / Learning SciPy
• CommentCount attribute / Preselection and processing of attributes
• complex classifiers
  • building / Building more complex classifiers
• complex dataset
  • about / A more complex dataset and a more complex classifier
• confusion matrix
  • used, for accuracy measurement in multiclass problems / Using the confusion matrix to measure accuracy in multiclass problems
• constants package / Learning SciPy
• correlation
  • about / Correlation
  • using / Correlation
• cost function
  • about / Building more complex classifiers
• CountVectorizer
  • about / Converting raw text into a bag-of-words
• Coursera
  • URL / Online courses
• CreationDate attribute / Preselection and processing of attributes
• cross-validation
  • about / Evaluation – holding out data and cross-validation
• cross-validation, for regression / Cross-validation for regression
• cross-validation schedule
  • about / Evaluation – holding out data and cross-validation
• Cross Validated
  • about / What to do when you are stuck, Q
  • URL / Q

D

• data
  • fetching / Fetching the data
  • slimming down, to chewable chunks / Slimming the data down to chewable chunks
• data, machine learning application
  • reading / Reading in the data
  • cleaning / Preprocessing and cleaning the data
  • preprocessing / Preprocessing and cleaning the data
• data analysis
  • jug, using for / Using jug for data analysis
• data sources, machine language / Data sources
• dimensionality reduction
  • about / Sketching our roadmap
• dot() function / Comparing runtime behaviors

E

• Elastic net model
  • about / L1 and L2 penalties
• Elastic nets
  • using, in scikit-Learn / Using Lasso or Elastic nets in scikit-learn
• ensemble learning / Combining multiple methods
• Enthought Python Distribution
  • URL / Installing Python

F

• false negative
  • about / Building more complex classifiers
• false positive
  • about / Building more complex classifiers
• Fast Fourier transformation / Learning SciPy
• feature engineering
  • about / What the book will teach you (and what it will not), Features and feature engineering
• feature extraction
  • about / Feature extraction
  • PCA / About principal component analysis (PCA)
  • LDA / Limitations of PCA and how LDA can help
• features
  • about / The Iris dataset, Features and feature engineering
  • engineering / Engineering the features
  • designing / Designing more features
  • computing, from images / Computing features from images
  • writing / Writing your own features
  • selecting / Selecting features
• feature selection
  • about / Features and feature engineering
• feature selection methods
  • about / Other feature selection methods
• FFT
  • used, for building classifier / Using FFT to build our first classifier, Increasing experimentation agility
• fftpack package / Learning SciPy
• filtering
  • for different effects / Filtering for different effects
• filters
  • used, for detecting features / Detecting redundant features using filters
  • disadvantage / Mutual information
• fit_transform() method / Limitations of PCA and how LDA can help
• flat clustering
  • about / Clustering

G

• Gaussian blurring
  • about / Gaussian blurring
• GaussianNB / Creating our first classifier and tuning it
• genfromtxt() function / Reading in the data
• gensim package
  • about / Building a topic model
• good answers
  • defining / Defining what is a good answer
• graphical processing units (GPUs) / Using Amazon Web Services (AWS)
• GTZAN dataset
  • about / Fetching the music data
  • URL, for downloading / Fetching the music data

H

• Haralick texture features
  • about / Computing features from images
• harder dataset
  • classifying / Classifying a harder dataset
• hierarchical clustering
  • about / Clustering
• hierarchical Dirichlet process (HDP) / Choosing the number of topics
• house prices
  • predicting, with regression / Predicting house prices with regression
• hyperparameters
  • setting / Setting hyperparameters in a smart way

I

• image processing
  • about / Introducing image processing
• images
  • loading / Loading and displaying images
  • displaying / Loading and displaying images
  • features, computing from / Computing features from images
• indexing, NumPy / Indexing
• installation, Python / Installing Python
• installation, Python packages
  • on Amazon Linux / Installing Python packages on Amazon Linux
• instance
  • about / Creating your first machines
• instance, classy answers
  • tuning / Tuning the instance
• integrate package / Learning SciPy
• interest point detection / Local feature representations
• International Society forMusic Information Retrieval (ISMIR) / Improving classification performance with Mel Frequency Cepstral Coefficients
• interpolate package / Learning SciPy
• io package / Learning SciPy
• Iris dataset
  • about / The Iris dataset
  • visualization / The first step is visualization

J

• JPEG
  • about / Introducing image processing
• jug
  • used for breaking up pipeline, into tasks / Using jug to break up your pipeline into tasks
  • about / Using jug to break up your pipeline into tasks
  • partial results, reusing / Reusing partial results
  • working / Looking under the hood
  • using, for data analysis / Using jug for data analysis
  • URL, for documentation / Using jug for data analysis
  • running, on cloud machine / Running jug on our cloud machine
• jug cleanup / Using jug for data analysis
• jug execute file / About tasks
• jugfile.jugdata directory / About tasks
• jugfile.py file / About tasks
• jug invalidate / Using jug for data analysis
• jug status --cache / Using jug for data analysis

K

• k-means clustering
  • about / Local feature representations
• k-nearest neighbor (kNN) algorithm
  • about / Starting with the k-nearest neighbor (kNN) algorithm
• Kaggle
  • URL / Getting competitive
• keys
  • about / Automating the generation of clusters with starcluster
• KMeans
  • about / KMeans

L

• labels
  • about / Learning to classify classy answers
• Laplace smoothing / Accounting for unseen words and other oddities
• Lasso
  • about / L1 and L2 penalties
  • using, in scikit-Learn / Using Lasso or Elastic nets in scikit-learn
• LDA
  • about / Latent Dirichlet allocation (LDA), Sketching our roadmap, Limitations of PCA and how LDA can help
• learning algorithm
  • selecting / Choosing the right model and learning algorithm, Before building our first model, Starting with a simple straight line, Towards some advanced stuff, Stepping back to go forward – another look at our data, Training and testing, Answering our initial question
• Levenshtein distance
  • about / How not to do it
• Lidstone smoothing
  • about / Accounting for unseen words and other oddities
• lift
  • about / Association rule mining
• linalg package / Learning SciPy
• LinearRegression class / Cross-validation for regression
• Load Sharing Facility (LSF) / Using jug to break up your pipeline into tasks
• local feature representations
  • about / Local feature representations
• logistic regression
  • using / Using logistic regression
  • example / A bit of math with a small example
  • applying, to postclassification problem / Applying logistic regression to our postclassification problem
• logistic regression classifier / Training the classifier
• loss function / Building more complex classifiers

M

• machine learning (ML)
  • goals / Machine learning and Python – the dream team
  • in real world / Rating prediction and recommendations
  • online courses / Online courses
  • books / Books
  • Q&A sites / Q
  • blogs / Blogs
  • data sources / Data sources
  • supervised learning competitions / Getting competitive
  • additional resources / What was left out
• machine learning application
  • about / Our first (tiny) machine learning application
  • data, reading / Reading in the data
  • data, preprocessing / Preprocessing and cleaning the data
  • data, cleaning / Preprocessing and cleaning the data
  • learning algorithm, selecting / Choosing the right model and learning algorithm, Before building our first model, Starting with a simple straight line, Towards some advanced stuff, Stepping back to go forward – another look at our data, Training and testing, Answering our initial question
• Machine Learning Repository / Data sources
• Machine Learning Toolkit (MILK)
  • URL / What was left out
• machines
  • creating / Creating your first machines
• Mahotas
  • about / Loading and displaying images, Thresholding
• mahotas.features / Computing features from images
• mahotas computer vision package
  • about / Loading and displaying images
• massive open online course (MOOC) / Online courses
• Matplotlib
  • about / Introduction to NumPy, SciPy, and Matplotlib, The first step is visualization, Looking at music
  • URL / Introduction to NumPy, SciPy, and Matplotlib
• matshow() function / Using the confusion matrix to measure accuracy in multiclass problems
• maxentropy package / Learning SciPy
• MDS
  • about / Sketching our roadmap, Multidimensional scaling (MDS)
• Mel Frequency Cepstral Coefficients
  • used, for improving classification performance / Improving classification performance with Mel Frequency Cepstral Coefficients
• Mel Frequency Cepstral Coefficients (MFCC) / Improving classification performance with Mel Frequency Cepstral Coefficients
• Mel Frequency Cepstrum (MFC) / Improving classification performance with Mel Frequency Cepstral Coefficients
• MetaOptimize
  • URL / Q
  • about / Q
• MetaOptimized
  • about / What to do when you are stuck
• mfcc() function / Improving classification performance with Mel Frequency Cepstral Coefficients
• mh.features.haralick function / Computing features from images
• MLComp
  • URL / Getting test data to evaluate our ideas on
• Modular toolkit for Data Processing (MDP)
  • URL / What was left out
• movie recommendation dataset
  • about / Improved recommendations
  • binary matrix of recommendations, using / Using the binary matrix of recommendations
  • movie neighbors, viewing / Looking at the movie neighbors
  • multiple methods, combining / Combining multiple methods
• MP3 files
  • converting, into wave format / Converting into a wave format
• multiclass classification
  • about / Binary and multiclass classification
• multiclass problems
  • confusion matrix, used for accuracy measurement / Using the confusion matrix to measure accuracy in multiclass problems
• multidimensional regression
  • about / Multidimensional regression
• MultinomialNB / Creating our first classifier and tuning it
• music
  • decomposing, into sine wave components / Decomposing music into sine wave components
• music data
  • fetching / Fetching the music data
• Music Information Retrieval (MIR) / Improving classification performance with Mel Frequency Cepstral Coefficients

N

• Naive Bayes
  • used, for classification / Using Naive Bayes to classify
• Naive Bayes classifier
  • about / Introducing the Naive Bayes classifier, Getting to know the Bayes theorem
  • accounting, for unseen words / Accounting for unseen words and other oddities
  • accounting, for oddities / Accounting for unseen words and other oddities
  • accounting, for arithmetic underflows / Accounting for arithmetic underflows
• Naive Bayes classifiers
  • GaussianNB / Creating our first classifier and tuning it
  • MultinomialNB / Creating our first classifier and tuning it
  • BernoulliNB / Creating our first classifier and tuning it
• ndimage (n-dimensional image)
  • about / Loading and displaying images
• ndimage package / Learning SciPy
• nearest neighbor classification
  • about / Nearest neighbor classification
• nearest neighbor search (NNS) / What the book will teach you (and what it will not)
• Netflix
  • about / Rating prediction and recommendations
• NLTK
  • installing / Installing and using NLTK
  • using / Installing and using NLTK
• NLTK's stemmer
  • used, for extending vectorizer / Extending the vectorizer with NLTK's stemmer
• norm() function / Counting words
• np.linalg.lstsq function / Predicting house prices with regression
• NumPy
  • URL, for tutorials / Chewing data efficiently with NumPy and intelligently with SciPy
  • learning / Learning NumPy
  • indexing / Indexing
  • non-existing values, handling / Handling non-existing values
  • runtime behaviors, comparing / Comparing runtime behaviors
  • about / Loading and displaying images

O

• odr package / Learning SciPy
• OpenCV
  • about / Loading and displaying images
• optimize package / Learning SciPy
• Oracle Grid Engine (OGE) / Using jug to break up your pipeline into tasks
• Otsu threshold
  • about / Thresholding
• overfitting
  • about / Towards some advanced stuff
• OwnerUserId attribute / Preselection and processing of attributes

P

• packages, SciPy
  • cluster / Learning SciPy
  • constants / Learning SciPy
  • fftpack / Learning SciPy
  • integrate / Learning SciPy
  • interpolate / Learning SciPy
  • io / Learning SciPy
  • linalg / Learning SciPy
  • maxentropy / Learning SciPy
  • ndimage / Learning SciPy
  • odr / Learning SciPy
  • optimize / Learning SciPy
  • signal / Learning SciPy
  • sparse / Learning SciPy
  • spatial / Learning SciPy
  • special / Learning SciPy
  • stats / Learning SciPy
• parameters
  • tweaking / Tweaking the parameters
• partial results
  • reusing / Reusing partial results
• Part Of Speech (POS) / Sketching our roadmap, Determining the word types
• pattern recognition
  • about / Pattern recognition
• PCA
  • about / Sketching our roadmap, About principal component analysis (PCA)
  • sketching / Sketching PCA
  • applying / Applying PCA
  • limitations / Limitations of PCA and how LDA can help
• pearsonr() function
  • about / Correlation
• penalized regression
  • about / Penalized regression
  • L2 penalty / L1 and L2 penalties
  • L1 penalty / L1 and L2 penalties
  • Lasso model / L1 and L2 penalties
  • Elastic net / L1 and L2 penalties
• Penn Treebank Project
  • URL / Determining the word types
• P greater than N scenarios
  • about / P greater than N scenarios
  • text example / An example based on text
  • hyperparameters, setting / Setting hyperparameters in a smart way
  • prediction, rating / Rating prediction and recommendations
  • recommendations, rating / Rating prediction and recommendations
• PNG
  • about / Introducing image processing
• polyfit() function / Starting with a simple straight line
• Portable Batch System (PBS) / Using jug to break up your pipeline into tasks
• postclassification problem
  • logistic regression, applying to / Applying logistic regression to our postclassification problem
• posts
  • relatedness, measuring / Measuring the relatedness of posts, How to do it
  • clustering / Clustering posts
• PostType attribute / Preselection and processing of attributes
• Pybrain
  • URL / What was left out
• pymining
  • about / More advanced basket analysis
• pyplot package / Preprocessing and cleaning the data
• Python
  • installing / Installing Python
  • about / Loading and displaying images
• Python packages
  • installing, on Amazon Linux / Installing Python packages on Amazon Linux

Q

• Q&A sites
  • about / What to do when you are stuck, Q
  • MetaOptimize / Q
  • Cross Validated / Q

R

• read_fft() function / Increasing experimentation agility
• receiver operator characteristic (ROC)
  • used, for measuring classifier performance / An alternate way to measure classifier performance using receiver operator characteristic (ROC)
  • about / An alternate way to measure classifier performance using receiver operator characteristic (ROC)
• redundant features
  • detecting, filters used / Detecting redundant features using filters
• redundant features detection
  • correlation, using / Correlation
  • mutual information / Mutual information
• regression
  • used, for predicting house prices / Predicting house prices with regression
• Ridge regression
  • about / L1 and L2 penalties
• Ridley-Calvard method
  • about / Thresholding
• root mean squared error (RMSE) / Predicting house prices with regression

S

• salt and pepper noise
  • adding / Adding salt and pepper noise
  • center, inserting in focus / Putting the center in focus
• save() function / Increasing experimentation agility
• Scikit
  • about / Clustering
• scikit-image (Skimage)
  • about / Loading and displaying images
• scikit-Learn
  • Lasso, using in / Using Lasso or Elastic nets in scikit-learn
  • Elastic nets, using in / Using Lasso or Elastic nets in scikit-learn
• SciPy
  • about / Introduction to NumPy, SciPy, and Matplotlib, Learning SciPy, Loading and displaying images
  • URL / Introduction to NumPy, SciPy, and Matplotlib
  • packages / Learning SciPy
• Score attribute / Preselection and processing of attributes
• Secure Shell (SSH)
  • about / Creating your first machines
• Securities and Exchange Commission (SEC)
  • about / An example based on text
• Seeds dataset
  • about / Learning about the Seeds dataset
• sentiment analysis, tweet / Sketching our roadmap
• SentiWordNet
  • URL / Successfully cheating using SentiWordNet
  • about / Successfully cheating using SentiWordNet
  • used, for cheating / Successfully cheating using SentiWordNet
• SIFT
  • about / Introducing image processing, Local feature representations
• signal package / Learning SciPy
• similarity
  • comparing, in topic space / Comparing similarity in topic space
• sine wave components
  • music, decomposing into / Decomposing music into sine wave components
• sklearn.feature_selection package / Asking the model about the features using wrappers
• sklearn.lda
  • about / Latent Dirichlet allocation (LDA)
• sklearn.naive_bayes package / Creating our first classifier and tuning it
• sklearn package / Converting raw text into a bag-of-words
• sobel filtering
  • about / Writing your own features
• sparse package / Learning SciPy
• sparsity
  • about / Building a topic model
• spatial package / Learning SciPy
• specgram() function / Looking at music
• special package / Learning SciPy
• spectrogram
  • about / Looking at music
• starcluster
  • cluster generation, automating with / Automating the generation of clusters with starcluster
• Starcluster
  • URL, for documentation / Automating the generation of clusters with starcluster
• stats package / Learning SciPy
• stemming
  • about / Stemming
  • NLTK, installing / Installing and using NLTK
  • NLTK, using / Installing and using NLTK
• supermarket shopping baskets
  • analyzing / Analyzing supermarket shopping baskets
• supervised learning
  • about / The Iris dataset
• support vector machines (SVM) / What the book will teach you (and what it will not)
• SURF
  • about / Local feature representations
• system
  • demonstrating, for new post / Solving our initial challenge, Another look at noise

T

• Talkbox SciKit / Improving classification performance with Mel Frequency Cepstral Coefficients
• task
  • about / About tasks
  • example / About tasks
• term frequency - inverse document frequency (TF-IDF) / Stop words on steroids
• testing error
  • about / Evaluation – holding out data and cross-validation
• text preprocessing phase
  • achievements / Our achievements and goals
  • goals / Our achievements and goals
• thresholding
  • about / Thresholding
• Title attribute / Preselection and processing of attributes
• topic model
  • about / Latent Dirichlet allocation (LDA), Comparing similarity in topic space
  • building / Building a topic model
• topic modeling
  • about / Choosing the number of topics
• topics
  • about / Latent Dirichlet allocation (LDA)
  • selecting / Choosing the number of topics
• topic space
  • similarity, comparing / Comparing similarity in topic space
• training error
  • about / Evaluation – holding out data and cross-validation
• transform method / Counting words
• tweets
  • cleaning / Cleaning tweets
• Twitter data
  • fetching / Fetching the Twitter data
• TwoToReal
  • about / Q
  • URL / Q

U

• University of California at Irvine (UCI)
  • about / Learning about the Seeds dataset

V

• vectorization
  • about / How to do it
• vectorizer
  • extending, with NLTK's stemmer / Extending the vectorizer with NLTK's stemmer
• ViewCount attribute / Preselection and processing of attributes
• visualization, Iris dataset / The first step is visualization

W

• wave format
  • MP3 files, converting into / Converting into a wave format
• Wikipedia
  • modeling / Modeling the whole of Wikipedia
  • URL, for dumps / Modeling the whole of Wikipedia
• word count vectors
  • normalizing / Normalizing the word count vectors
• wordle
  • URL / Building a topic model
• word sense disambiguation / Successfully cheating using SentiWordNet
• word types
  • about / Taking the word types into account
  • determining / Determining the word types
• wrappers
  • using / Asking the model about the features using wrappers