Textbook in PDF format

What Every Engineer Should Know About Data-Driven Analytics provides a comprehensive introduction to the theoretical concepts and approaches of Machine Learning that are used in predictive data analytics. By introducing the theory and by providing practical applications, this text can be understood by every engineering discipline. It offers a detailed and focused treatment of the important Machine Learning (ML) approaches and concepts that can be exploited to build models to enable decision making in different domains.
A brief introduction to the basics of R and Python programming is provided here, which will be very helpful for the readers to navigate through the other chapters in this book. Now let’s look at some basics of the Python programming. Readers are recommended to execute the provided scripts here in the Python terminal or Jupyter Notebook. In the Chapter 1, the objective is merely to provide an exposure to programming in R and Python so that the readers can become familiar with the syntax used in R and Python scripts. In the latter chapters, R and Python scripts will be used interchangeably to illustrate the concepts discussed.
Several essential packages in R and Python that will be very helpful for performing data wrangling (structuring and cleaning data) and for performing analytics are listed below. In the latter chapters several of these listed packages will be used to demonstrate their potential for data wrangling and analytics.
1. Data Collection and Cleaning
2. Mathematical Background for Predictive Analytics
3. Introduction to Statistics, Probability, and Information Theory for Analytics. 4. Introduction to Machine Learning
5. Unsupervised Learning
6. Supervised Learning
7. Natural Language Processing for Analyzing Unstructured Data
8. Predictive Analytics Using Deep Neural Networks
9. Convolutional Neural Networks (CNN) for Predictive Analytics
10. Recurrent Neural Networks (RNNs) for Predictive Analytics
11. Recommender Systems for Predictive Analytics
12. Architecting Big Data Analytical Pipeline