What Every Engineer Should Know About Data-Driven Analytics

Table of contents

1. Cover Page
2. Half Title page
3. Series Page
4. Title Page
5. Copyright Page
6. Dedication
7. Contents
8. Preface
   1. Introduction
   2. Audience
   3. Course Adoption
   4. Errors
9. Acknowledgments
10. About the Authors
11. 1 Data Collection and Cleaning
    1. Data-Collection Strategies
    2. Data Preprocessing Strategies
    3. Programming with R
       1. Data Types in R
       2. Data Structures in R
       3. Package Installation in R
       4. Reading and Writing Data in R
       5. Using the FOR Loop in R
       6. Using the WHILE Loop in R
       7. Using the IF-ELSE Statement in R
    4. Programming with Python
    5. Data Wrangling and Analytics in R and Python
    6. Structuring and Cleaning Data
       1. Missing Data
       2. Strategies for Dealing with Missing Data
    7. Data Deduplication
    8. Summary
    9. Exercise
    10. Notes
    11. References
12. 2 Mathematical Background for Predictive Analytics
    1. Basics of Linear Algebra
       1. Vectors and Matrices
       2. Determinant
    2. Simple Linear Regression (SLR)
    3. Principal Component Analysis (PCA)
    4. Singular Value Decomposition (SVD)
    5. Introduction to Neural Networks
    6. Summary
    7. Exercise
    8. References
13. 3 Introduction to Statistics, Probability, and Information Theory for Analytics
    1. Normal Distribution and the Central Limit Theorem
    2. Pearson Correlation Coefficient and Covariance
    3. Basic Probability for Predictive Analytics
    4. Conditional Probability
    5. Bayes’ Theorem and Bayesian Classifiers
    6. Information Theory for Predictive Modeling
    7. Summary
    8. Exercise
    9. Notes
    10. References
14. 4 Introduction to Machine Learning
    1. Statistical versus Machine Learning Models
    2. Regression Techniques
    3. Multiple Linear Regression (MLR) Model
    4. Assumptions of MLR
    5. Introduction to Multinomial Logistic Regression (MLogR)
       1. Bias versus Variance Trade-off
       2. Overfitting and Underfitting
       3. Regularization
          1. Ridge Regression
          2. Lasso Regression
    6. Summary
    7. Exercise
    8. Notes
    9. References
15. 5 Unsupervised Learning
    1. K-means Clustering
    2. Hierarchical Clustering
    3. Association Rule Mining
    4. K-Nearest Neighbors
    5. Summary
    6. Exercise
    7. References
16. 6 Supervised Learning
    1. Introduction to Artificial Neural Networks
    2. Forward and Backward Propagation Methods
       1. Architectural Types in ANN
       2. Hyperparameters for Tuning the ANN
       3. An Example of ANN Classification
    3. Introduction to Ensemble Learning Techniques
       1. Random Forest Ensemble Learning
       2. Introduction to AdaBoost Ensemble Learning
       3. Introduction to Extreme Gradient Boosting (XGB)
    4. Cross-Validation
    5. Summary
    6. Exercise
    7. References
17. 7 Natural Language Processing for Analyzing Unstructured Data
    1. Terminology for NLP
    2. Installing NLTK and Other Libraries
    3. Tokenization
    4. Stemming
    5. Stopwords
    6. Part of Speech Tagging
    7. Bag-of-Words (BOW)
    8. n-grams
    9. Sentiment and Emotion Classification
    10. Summary
    11. Exercise
    12. References
18. 8 Predictive Analytics Using Deep Neural Networks
    1. Introduction to Deep Learning
    2. The Deep Neural Networks and Its Architectural Variants
    3. Multilayer Perceptron (MLP)
    4. Convolutional Neural Networks (CNN)
    5. Recurrent Neural Networks (RNN)
    6. AlexNet
    7. VGGNet
    8. Inception
    9. ResNet and GoogLeNet
    10. Hyperparameters of DNN and Strategies for Tuning Them
    11. Activation Function
    12. Regularization
    13. Number of Hidden Layers
    14. Number of Neurons Per Layer
    15. Learning Rate
    16. Optimizer
    17. Batch Size
    18. Epoch
    19. Weight and Biases Initialization
    20. Grid Search
    21. Random Search
    22. Deep Belief Networks (DBN)
    23. Analyzing the Boston Housing Dataset Using DNN
    24. Summary
    25. Exercise
    26. References
19. 9 Convolutional Neural Networks (CNN) for Predictive Analytics
    1. Convolution Layer
    2. Padding and Strides
    3. ReLU LAYER
    4. Pooling Layer
    5. Fully Connected Layer
    6. Hyperparameters of CNNs
    7. Image Classification Using a CNN Model Based on LeNet Architecture
    8. Summary
    9. Exercise
    10. References
20. 10 Recurrent Neural Networks (RNNs) for Predictive Analytics
    1. Recurrent Neural Networks
       1. Long Short-Term Memory
       2. Forget Gate
       3. Input Gate
       4. Output Gate
       5. More Details of the LSTM
       6. Hyperparameters for RNNs
    2. Summary
    3. Exercise
    4. References
21. 11 Recommender Systems for Predictive Analytics
    1. Content-Based Filtering
    2. Cosine Similarity
    3. Collaborative Filtering
       1. User-Based Collaborative Filtering (UBCF)
       2. Item-Based Collaborative Filtering (IBCF)
    4. Hybrid Recommendation Systems
       1. Examples of Using Hybrid Recommendation Systems
    5. Summary
    6. Exercise
    7. References
22. 12 Architecting Big Data Analytical Pipeline
    1. Big Data Technology Landscape and Analytics Platform
    2. Data Pipeline Architecture
    3. Lambda Architecture
    4. Twitter and Pinterest’s Data Pipeline Architecture
    5. Design Strategies for Building Customized Big Data Pipeline
    6. Design Patterns and Pattern Languages
    7. Summary
    8. Exercise
    9. References
23. Glossary of Terms
24. Index