Become a Data Scientist in 30 Days!

This book guides software developers to understand the theory underlying Artificial Intelligence (AI) and Machine Learning (ML) in a simplified manner. With consistent daily practice, readers can indeed succeed in becoming data scientists in 30 days. The book provides step by step development training to practice and master the AI/ML technology - multilayer perceptron deep learning neural networks - that is currently in high demand worldwide. The examples utilize real industry data published by Fannie Mae. Although this book uses the mortgage finance industry data for most of the practical training, the techniques it introduces are quite generic in nature and apply to other industries such as media, healthcare, environmental science, and manufacturing. The code used in the examples include state of the art tools built in Python by industry leaders like Google, Amazon, Apple, Facebook, and IBM. This book is carefully designed to enhance online learning for busy professionals and students to provide valuable research results on literature and publications in order to accelerate skill development. The developers are able to start coding on day one and see results immediately. The code is also available for free download. Readers will be able to build supervised learning models of their own using industry data sets either at their workplace or from other published data sources such as: Mortgage data by Fannie Mae, weather data from NOAA, air quality data from EPA, health data from NIH, or any other sources of their professional interest. This book will not only be beneficial for high school and college students pursuing a computer science and engineering education, but also for existing IT professionals and academics seeking to expand their knowledge base. Researchers, educators, technology managers, software developers, business analysts, and statisticians will all find value in this book. This book is the beginning of a series that breaks down the AI/ML study materials into easily consumable lessons. It is written for the busy students and professionals on an accelerated learning and skill development path. Subsequent books will follow a similar format to cover other AI/ML topics like Natural Language Processing (NLP), unsupervised learning, vision and process automation. The author has worked in the Information Technology (IT) industry for four decades since receiving his Doctor of Engineering degree in Computer Science and Artificial Intelligence. Many businesses utilize the author's consulting services to benefit from his thought leadership and out of the box thinking in solving complex engineering problems. Every business is going through the process of re-tooling to gear up for the digital transformation. This digital transformation is the intensive change of organizational activities, processes, and competencies to improve the way we conduct businesses. The goal of digital strategy is enhancing the customer experience and increasing the organization's competitive advantage. Data is at the center of this transformation, and demand for skilled professionals in the data science field is growing rapidly. The future for data scientists is very bright. By gaining the necessary skills and consistently practicing them will certainly lead to professional success.