Machine Learning Quick Reference Quick and Essential Machine Learning Hacks for Training Smart Data Models.
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 net...
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| Format: | eBook |
| Language: | English |
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Birmingham
Packt Publishing Ltd,
2019.
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| Online Access: | EBSCOhost Перейти в каталог НБ ТГУ |
Table of Contents:
- 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
- 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
- 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
- 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
- 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