Machine Learning Quick Reference: Quick and Essential Machine Learning Hacks for Training Smart Data Models.

Kumar  Rahul.

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dc.contributor.author	Kumar Rahul.
dc.date.accessioned	2024-01-26T21:31:39Z
dc.date.available	2024-01-26T21:31:39Z
dc.date.issued	2019
dc.identifier.citation	Kumar. Machine Learning Quick Reference: Quick and Essential Machine Learning Hacks for Training Smart Data Models. - Birmingham: Packt Publishing Ltd, 2019 - 1 online resource (283 p.) - URL: https://libweb.kpfu.ru/ebsco/pdf/2018975.pdf
dc.identifier.isbn	9781788831611
dc.identifier.isbn	1788831616
dc.identifier.uri	https://dspace.kpfu.ru/xmlui/handle/net/178210
dc.description	Description based upon print version of record.
dc.description	Importing the library
dc.description.abstract	Machine learning involves development and training of models used to predict future outcomes. This book is a practical guide to all the tips and tricks related to machine learning. It includes hands-on, easy to access techniques on topics like model selection, performance tuning, training neural networks, time series analysis and a lot more.
dc.description.tableofcontents	Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Quantifying Learning Algorithms; Statistical models; Learning curve; Machine learning; Wright's model; Curve fitting; Residual; Statistical modeling - the two cultures of Leo Breiman; Training data development data -- test data; Size of the training, development, and test set; Bias-variance trade off; Regularization; Ridge regression (L2); Least absolute shrinkage and selection operator ; Cross-validation and model selection; K-fold cross-validation
dc.description.tableofcontents	Model selection using cross-validation0.632 rule in bootstrapping; Model evaluation; Confusion matrix; Receiver operating characteristic curve; Area under ROC; H-measure; Dimensionality reduction; Summary; Chapter 2: Evaluating Kernel Learning; Introduction to vectors; Magnitude of the vector; Dot product; Linear separability; Hyperplanes ; SVM; Support vector; Kernel trick; Kernel; Back to Kernel trick; Kernel types; Linear kernel; Polynomial kernel; Gaussian kernel; SVM example and parameter optimization through grid search; Summary; Chapter 3: Performance in Ensemble Learning
dc.description.tableofcontents	What is ensemble learning?Ensemble methods ; Bootstrapping; Bagging; Decision tree; Tree splitting; Parameters of tree splitting; Random forest algorithm; Case study; Boosting; Gradient boosting; Parameters of gradient boosting; Summary; Chapter 4: Training Neural Networks; Neural networks; How a neural network works; Model initialization; Loss function; Optimization; Computation in neural networks; Calculation of activation for H1; Backward propagation; Activation function; Types of activation functions; Network initialization; Backpropagation; Overfitting; Prevention of overfitting in NNs
dc.description.tableofcontents	Vanishing gradient Overcoming vanishing gradient; Recurrent neural networks; Limitations of RNNs; Use case; Summary; Chapter 5: Time Series Analysis; Introduction to time series analysis; White noise; Detection of white noise in a series; Random walk; Autoregression; Autocorrelation; Stationarity; Detection of stationarity; AR model; Moving average model; Autoregressive integrated moving average; Optimization of parameters; AR model; ARIMA model; Anomaly detection; Summary; Chapter 6: Natural Language Processing; Text corpus; Sentences; Words; Bags of words; TF-IDF
dc.description.tableofcontents	Executing the count vectorizerExecuting TF-IDF in Python; Sentiment analysis; Sentiment classification; TF-IDF feature extraction; Count vectorizer bag of words feature extraction; Model building count vectorization; Topic modeling ; LDA architecture; Evaluating the model; Visualizing the LDA; The Naive Bayes technique in text classification; The Bayes theorem; How the Naive Bayes classifier works; Summary; Chapter 7: Temporal and Sequential Pattern Discovery; Association rules; Apriori algorithm; Finding association rules; Frequent pattern growth; Frequent pattern tree growth; Validation
dc.language	English
dc.language.iso	en
dc.publisher	Birmingham Packt Publishing Ltd
dc.subject.other	Machine learning.
dc.subject.other	COMPUTERS / General
dc.subject.other	Electronic books.
dc.title	Machine Learning Quick Reference: Quick and Essential Machine Learning Hacks for Training Smart Data Models.
dc.type	Book
dc.description.pages	1 online resource (283 p.)
dc.collection	Электронно-библиотечные системы
dc.source.id	EN05CEBSCO05C232