book

What Every Engineer Should Know About Data-Driven Analytics

by Satish Mahadevan Srinivasan, Phillip A. Laplante

April 2023

Beginner

278 pages

7h 29m

English

CRC Press

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IntroductionAudienceCourse AdoptionErrors

Data-Collection StrategiesData Preprocessing StrategiesProgramming with RData Types in RData Structures in RPackage Installation in RReading and Writing Data in RUsing the FOR Loop in RUsing the WHILE Loop in RUsing the IF-ELSE Statement in RProgramming with PythonData Wrangling and Analytics in R and PythonStructuring and Cleaning DataMissing DataStrategies for Dealing with Missing DataData DeduplicationSummaryExerciseNotesReferences
Basics of Linear AlgebraVectors and MatricesDeterminantSimple Linear Regression (SLR)Principal Component Analysis (PCA)Singular Value Decomposition (SVD)Introduction to Neural NetworksSummaryExerciseReferences
Normal Distribution and the Central Limit TheoremPearson Correlation Coefficient and CovarianceBasic Probability for Predictive AnalyticsConditional ProbabilityBayes’ Theorem and Bayesian ClassifiersInformation Theory for Predictive ModelingSummaryExerciseNotesReferences
Statistical versus Machine Learning ModelsRegression TechniquesMultiple Linear Regression (MLR) ModelAssumptions of MLRIntroduction to Multinomial Logistic Regression (MLogR)Bias versus Variance Trade-offOverfitting and UnderfittingRegularizationRidge RegressionLasso RegressionSummaryExerciseNotesReferences
K-means ClusteringHierarchical ClusteringAssociation Rule MiningK-Nearest NeighborsSummaryExerciseReferences
Introduction to Artificial Neural NetworksForward and Backward Propagation MethodsArchitectural Types in ANNHyperparameters for Tuning the ANNAn Example of ANN ClassificationIntroduction to Ensemble Learning TechniquesRandom Forest Ensemble LearningIntroduction to AdaBoost Ensemble LearningIntroduction to Extreme Gradient Boosting (XGB)Cross-ValidationSummaryExerciseReferences
Terminology for NLPInstalling NLTK and Other LibrariesTokenizationStemmingStopwordsPart of Speech TaggingBag-of-Words (BOW)n-gramsSentiment and Emotion ClassificationSummaryExerciseReferences
Introduction to Deep LearningThe Deep Neural Networks and Its Architectural VariantsMultilayer Perceptron (MLP)Convolutional Neural Networks (CNN)Recurrent Neural Networks (RNN)AlexNetVGGNetInceptionResNet and GoogLeNetHyperparameters of DNN and Strategies for Tuning ThemActivation FunctionRegularizationNumber of Hidden LayersNumber of Neurons Per LayerLearning RateOptimizerBatch SizeEpochWeight and Biases InitializationGrid SearchRandom SearchDeep Belief Networks (DBN)Analyzing the Boston Housing Dataset Using DNNSummaryExerciseReferences
Convolution LayerPadding and StridesReLU LAYERPooling LayerFully Connected LayerHyperparameters of CNNsImage Classification Using a CNN Model Based on LeNet ArchitectureSummaryExerciseReferences
Recurrent Neural NetworksLong Short-Term MemoryForget GateInput GateOutput GateMore Details of the LSTMHyperparameters for RNNsSummaryExerciseReferences
Content-Based FilteringCosine SimilarityCollaborative FilteringUser-Based Collaborative Filtering (UBCF)Item-Based Collaborative Filtering (IBCF)Hybrid Recommendation SystemsExamples of Using Hybrid Recommendation SystemsSummaryExerciseReferences
Big Data Technology Landscape and Analytics PlatformData Pipeline ArchitectureLambda ArchitectureTwitter and Pinterest’s Data Pipeline ArchitectureDesign Strategies for Building Customized Big Data PipelineDesign Patterns and Pattern LanguagesSummaryExerciseReferences

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Glossary of Terms

activation function: in ANN, the activation function of a node defines the output of that node given an input or set of inputs.
AdaBoost: an ensemble learning method that uses an iterative approach to learn from the mistakes of weak classifiers and turn them into strong ones.
agglomerative hierarchical clustering: a bottom-up approach in which each observation starts as its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
analysis of variance (ANOVA): an analysis tool used in statistics that splits an observed aggregate variability found inside a dataset into two parts: systematic factors and random factors.
Apache Spark: a multilanguage engine for executing data engineering, data science, and machine learning ...