What every engineer should know about data-driven analytics

What every engineer should know about data-driven analytics

What Every Engineer Should Know About Data-Driven Analytics provides a comprehensive introduction to the machine learning theoretical concepts and approaches that are used in predictive data analytics through practical applications and case studies.

Detalles Bibliográficos
Otros Autores: Laplante, Phillip A., author (author), Srinivasan, Satish Mahadevan, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, Florida : CRC Press [2023]
Edición:First edition
Colección:What every engineer should know.
Materias:
Machine learning.
Data mining.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009757938406719
Tabla de Contenidos:
  • Cover
  • Half Title
  • Series Page
  • Title Page
  • Copyright Page
  • Dedication
  • Table of Contents
  • Preface
  • Introduction
  • Audience
  • Course Adoption
  • Errors
  • Acknowledgments
  • About the Authors
  • Chapter 1: Data Collection and Cleaning
  • Data-Collection Strategies
  • Data Preprocessing Strategies
  • Programming with R
  • Data Types in R
  • Data Structures in R
  • Package Installation in R
  • Reading and Writing Data in R
  • Using the FOR Loop in R
  • Using the WHILE Loop in R
  • Using the IF-ELSE Statement in R
  • Programming with Python
  • Data Wrangling and Analytics in R and Python
  • Structuring and Cleaning Data
  • Missing Data
  • Strategies for Dealing with Missing Data
  • Data Deduplication
  • Summary
  • Exercise
  • Notes
  • References
  • Chapter 2: Mathematical Background for Predictive Analytics
  • Basics of Linear Algebra
  • Vectors and Matrices
  • Determinant
  • Simple Linear Regression (SLR)
  • Principal Component Analysis (PCA)
  • Singular Value Decomposition (SVD)
  • Introduction to Neural Networks
  • Summary
  • Exercise
  • References
  • Chapter 3: Introduction to Statistics, Probability, and Information Theory for Analytics
  • Normal Distribution and the Central Limit Theorem
  • Pearson Correlation Coefficient and Covariance
  • Basic Probability for Predictive Analytics
  • Conditional Probability
  • Bayes' Theorem and Bayesian Classifiers
  • Information Theory for Predictive Modeling
  • Summary
  • Exercise
  • Notes
  • References
  • Chapter 4: Introduction to Machine Learning
  • Statistical versus Machine Learning Models
  • Regression Techniques
  • Multiple Linear Regression (MLR) Model
  • Assumptions of MLR
  • Introduction to Multinomial Logistic Regression (MLogR)
  • Bias versus Variance Trade-off
  • Overfitting and Underfitting
  • Regularization
  • Ridge Regression
  • Lasso Regression
  • Summary
  • Exercise.
  • Notes
  • References
  • Chapter 5: Unsupervised Learning
  • K -means Clustering
  • Hierarchical Clustering
  • Association Rule Mining
  • K -Nearest Neighbors
  • Summary
  • Exercise
  • References
  • Chapter 6: Supervised Learning
  • Introduction to Artificial Neural Networks
  • Forward and Backward Propagation Methods
  • Architectural Types in ANN
  • Hyperparameters for Tuning the ANN
  • An Example of ANN Classification
  • Introduction to Ensemble Learning Techniques
  • Random Forest Ensemble Learning
  • Introduction to AdaBoost Ensemble Learning
  • Introduction to Extreme Gradient Boosting (XGB)
  • Cross-Validation
  • Summary
  • Exercise
  • References
  • Chapter 7: Natural Language Processing for Analyzing Unstructured Data
  • Terminology for NLP
  • Installing NLTK and Other Libraries
  • Tokenization
  • Stemming
  • Stopwords
  • Part of Speech Tagging
  • Bag-of-Words (BOW)
  • n- grams
  • Sentiment and Emotion Classification
  • Summary
  • Exercise
  • References
  • Chapter 8: Predictive Analytics Using Deep Neural Networks
  • Introduction to Deep Learning
  • The Deep Neural Networks and Its Architectural Variants
  • Multilayer Perceptron (MLP)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • AlexNet
  • VGGNet
  • Inception
  • ResNet and GoogLeNet
  • Hyperparameters of DNN and Strategies for Tuning Them
  • Activation Function
  • Regularization
  • Number of Hidden Layers
  • Number of Neurons Per Layer
  • Learning Rate
  • Optimizer
  • Batch Size
  • Epoch
  • Weight and Biases Initialization
  • Grid Search
  • Random Search
  • Deep Belief Networks (DBN)
  • Analyzing the Boston Housing Dataset Using DNN
  • Summary
  • Exercise
  • References
  • Chapter 9: Convolutional Neural Networks (CNN) for Predictive Analytics
  • Convolution Layer
  • Padding and Strides
  • ReLU LAYER
  • Pooling Layer
  • Fully Connected Layer.
  • Hyperparameters of CNNs
  • Image Classification Using a CNN Model Based on LeNet Architecture
  • Summary
  • Exercise
  • References
  • Chapter 10: Recurrent Neural Networks (RNNs) for Predictive Analytics
  • Recurrent Neural Networks
  • Long Short-Term Memory
  • Forget Gate
  • Input Gate
  • Output Gate
  • More Details of the LSTM
  • Hyperparameters for RNNs
  • Summary
  • Exercise
  • References
  • Chapter 11: Recommender Systems for Predictive Analytics
  • Content-Based Filtering
  • Cosine Similarity
  • Collaborative Filtering
  • User-Based Collaborative Filtering (UBCF)
  • Item-Based Collaborative Filtering (IBCF)
  • Hybrid Recommendation Systems
  • Examples of Using Hybrid Recommendation Systems
  • Summary
  • Exercise
  • References
  • Chapter 12: Architecting Big Data Analytical Pipeline
  • Big Data Technology Landscape and Analytics Platform
  • Data Pipeline Architecture
  • Lambda Architecture
  • Twitter and Pinterest's Data Pipeline Architecture
  • Design Strategies for Building Customized Big Data Pipeline
  • Design Patterns and Pattern Languages
  • Summary
  • Exercise
  • References
  • Glossary of Terms
  • Index.

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