Index
A
B
- back propagation (backprop) algorithm / Back propagation
- bagging
- bar plot
- basic inheritance
- basic terminology
- BatchIterator instance
- Bayes' theorem / Bayes' theorem
- behaviors
- Berkeley Vision and Learning Center (BVLC)
- bias
- big data
- blog posts
- Bokeh
- about / Bokeh
- differences, with matplotlib / Bokeh
- plots, creating with / Bokeh
- boosting / Leveraging weak learners via adaptive boosting
- bootstrap aggregating
- Breast Cancer Wisconsin dataset
- built-ins
- built-in scope
C
- Caffe
- CamelCase notation / Creating Python classes
- CAPTCHA
- CART (Classification and Regression Trees)
- case study, object-oriented design
- categorical data
- character classes / Matching a selection of characters
- character n-grams
- CIFAR-10
- class
- classes
- classification
- classification algorithm
- classification error / Maximizing information gain – getting the most bang for the buck
- classifiers
- class probabilities, modeling via logistic regression
- closed problem
- cluster evaluation
- clustering
- coassociation matrix
- code
- code coverage
- code coverage test
- command pattern
- complex algorithms
- composite pattern
- composition
- comprehensions
- computational tools
- concurrency
- confidence
- confusion matrix
- connected components
- constructor / Initializing the object
- context manager
- contour plot
- coroutines
- Cosine distance
- Counter object / Counter
- Coval font I, Open Font Library
- coverage.py / How much testing is enough?
- CPU
- Cron
- cross-fold validation framework
- CSV (comma-separated values)
- CSV (Comma Separated Values)
- csvkit tool / Data munging
- curse of dimensionality
D
- data
- data, blogging
- data, Corpus
- data, in binary format
- data, in MongoDB
- data, in Redis
- data, in text format
- data aggregation
- data analysis
- Dataframe
- DataFrame
- data mining
- data munging
- data notation / Serializing web objects
- data processing, using arrays
- dataset
- loading / Loading the dataset, Loading the dataset, An introduction to Lasagne
- data, collecting / Collecting the data
- URL / Collecting the data
- loading, pandas used / Using pandas to load the dataset
- cleaning up / Cleaning up the dataset
- new features, extracting / Extracting new features
- classifying, with existing model / Classifying with an existing model
- follower information, obtaining from Twitter / Getting follower information from Twitter
- network, building / Building the network
- graph, creating / Creating a graph
- Similarity graph, creating / Creating a similarity graph
- creating / Creating the dataset
- CAPTCHAs, drawing / Drawing basic CAPTCHAs
- image, splitting into individual letters / Splitting the image into individual letters
- training dataset, creating / Creating a training dataset
- training dataset, adjusting to methodology / Adjusting our training dataset to our methodology
- partitioning, in training and test sets / Partitioning a dataset in training and test sets
- datasets
- data structure, Pandas
- data types
- date and time objects
- decision regions / Training a perceptron via scikit-learn
- decision tree implementation
- decision tree learning
- decision tree regression
- decision trees
- decision trees classifiers
- decorator pattern
- decorators
- depth parameter / Fine-tuning machine learning models via grid search
- design patterns
- Dewey Decimal System (DDS)
- dictionaries
- dictionary
- dictionary comprehension
- DictVectorizer class
- dimensionality reduction
- disambiguation
- discretization
- discretization algorithm
- docstrings
- documents
- dot notation / Adding attributes
- DRY (Don't Repeat Yourself) principle / How do we use modules and packages
- duck typing
- dummy feature / Performing one-hot encoding on nominal features
E
F
- f1-score
- facade pattern
- factorial
- false positive rate (FPR) / Optimizing the precision and recall of a classification model
- fancy indexing
- FASTLab
- feature-based normalization
- feature creation
- feature extraction
- feature importance
- features, dataset
- features, Python
- feature scaling
- feature selection
- feed-forward neural network
- FIFO (First In First Out) queues
- file I/O
- filename, data
- flower dataset
- flyweight pattern
- FP-growth algorithm
- frequent itemsets
- funcargs / A completely different way to set up variables
- function
- functions
- functions, transformer
- function words
- futures
G
H
I
J
K
- k-fold cross-validation
- k-means algorithm
- k-nearest neighbor classifier (KNN)
- K-nearest neighbors
- k-nearest neighbors (KNN) algorithm
- Kaggle
- karma
- Keras
- kernel
- kernel functions
- kernel parameter
- kernel principal component analysis
- kernel principal component analysis, examples
- kernel principal component analysis, scikit-learn
- kernels / Kernels
- kernel SVM
- kernel trick
- KNN algorithm
L
M
N
- n-gram
- n-grams
- Naive Bayes
- Naive Bayes algorithm
- Naive Bayes model
- NameError exception / Scopes
- names
- namespaces
- NaN (Not a Number)
- National Basketball Association (NBA)
- Natural language processing toolkit (NLTK)
- Natural Language ToolKit (NLTK)
- nearest neighbor
- Nearest neighbors
- nested cross-validation
- network
- networks
- NetworkX
- NetworkX package
- neural network
- neural network layers, Lasagne
- neural networks
- Neural networks
- neurons
- news articles
- NLTK
- NLTK installation instructions
- nolearn package
- nominal features
- non-empty classes / Maximizing information gain – getting the most bang for the buck
- nonlinear mappings
- nonlinear problems, solving with kernel SVM
- nonlinear relationships
- nonparametric models
- normal equation / Estimating the coefficient of a regression model via scikit-learn
- normalization
- notations
- not equal (ne) function / Binary operations
- NumPy
- NumPy arrays
- n_neighbors
O
P
Q
R
S
T
U
- UCL Machine Learning data repository
- UDP (User Datagram Protocol) / AsyncIO for networking
- UML sequence diagram
- underfitting
- Unified Modeling Language (UML)
- unit tests
- univariate feature
- unstructured format
- unsupervised dimensionality reduction, via principal component analysis
- unsupervised learning
- use cases, computer vision
V
- V's, big data
- validation curves
- validation dataset
- value / Dictionaries
- values
- variance
- vectorization
- virtualenv
- visualization toolkit (VTK) / MayaVi
- vocabulary
- Vowpal Wabbit
W
X
Z
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