A Greater Foundation for Machine Learning Engineering

About the Book

This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised machine learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector machines lay a more excellent foundation for statistics. The author of the book Dr. Ganapathi Pulipaka, a top influencer of machine learning in the US, has created this as a reference book for universities. This book contains an incredible foundation for machine learning and engineering beyond a compact manual. The author goes to extraordinary lengths to make academic machine learning and deep learning literature comprehensible to create a new body of knowledge. The book aims at readership from university students, enterprises, data science beginners, machine learning and deep learning engineers at scale for high-performance computing environments. A Greater Foundation of Machine Learning Engineering covers a broad range of classical linear algebra and calculus with program implementations in PyTorch, TensorFlow, R, and Python with in-depth coverage. The author does not hesitate to go into math equations for each algorithm at length that usually many foundational machine learning books lack leveraging the JupyterLab environment. Newcomers can leverage the book from University or people from all walks of data science or software lives to the advanced practitioners of machine learning and deep learning. Though the book title suggests machine learning, there are several implementations of deep learning algorithms, including deep reinforcement learning.

The book's mission is to help build a strong foundation for machine learning and deep learning engineers with all the algorithms, processors to train and deploy into production for enterprise-wide machine learning implementations. This book also introduces all the concepts of natural language processing required for machine learning algorithms in Python. The book covers Bayesian statistics without assuming high-level mathematics or statistics experience from the readers. It delivers the core concepts and implementations required with R code with open datasets. The book also covers unsupervised machine learning algorithms with association rules and k-means clustering, metal-learning algorithms, bagging, boosting, random forests, and ensemble methods.

The book delves into the origins of deep learning in a scholarly way covering neural networks, restricted Boltzmann machines, deep belief networks, autoencoders, deep Boltzmann machines, LSTM, and natural language processing techniques with deep learning algorithms and math equations. It leverages the NLTK library of Python with PyTorch, Python, and TensorFlow's installation steps, then demonstrates how to build neural networks with TensorFlow. Deploying machine learning algorithms require a blend of cloud computing platforms, SQL databases, and NoSQL databases. Any data scientist with a statistics background that looks to transition into a machine learning engineer role requires an in-depth understanding of machine learning project implementations on Amazon, Google, or Microsoft Azure cloud computing platforms. The book provides real-world client projects for understanding the complete implementation of machine learning algorithms.

This book is a marvel that does not leave any application of machine learning and deep learning algorithms. It sets a more excellent foundation for newcomers and expands the horizons for experienced deep learning practitioners. It is almost inevitable that there will be a series of more advanced algorithms follow-up books from the author in some shape or form after setting such a perfect foundation for machine learning engineering.

About the Author

Dr. Ganapathi Pulipaka, the Chief AI Scientist and Chief Data Scientist of DeepSingularity, is distinguished in his field of machine learning and mathematics with many accolades and experience to his credit, and was born on August 20th, 1974. Dr. GP is an American premier speaker on machine learning, deep learning, robotics, IoT, and data science, and has been a keynote speaker in many North American top AI events. Top-ranked machine learning expert and best-selling author of two books on Amazon.

Top 50 Tech Leaders Award in Mathematics, Machine Learning, Data Science, and Big Data Analytics 2019, by InterCon World located in Santa Monica, California, a leading global technology conference that brings the brightest minds in technology by recognizing the data scientists and thought leaders in this area who empower hundreds and thousands of data science enthusiasts around the world with latest advancements. Top 50 Tech Visionaries Award in Mathematics, Machine Learning, Data Science, and Big Data Analytics 2020 by InterCon World located in Santa Monica, California, a leading global technology conference that brings the brightest minds in technology by recognizing the data scientists and thought leaders in this area who empower hundreds and thousands of data science enthusiasts around the world with latest advancements.

Marquis Who's Who in America 2020, a biographical volume award received by top 1 percent in America, inducted by Marquis Who’s Who since 1898 for Dr. GP’s noteworthy accomplishments, visibility, and prominence in his field of machine learning.

Number #1 in the following fields in 2020:

Machine Learning Authority in the World by Agilience
Analytics Authority in the World by Agilience
Data Management in the World by Agilience
Information Management in the World by Agilience
Top Machine Learning Influencer for 2020 by Verdict UK
Top Big Data Influencer for 2020 by Verdict UK 2020
Top Machine Learning Researcher by Mirror Review Magazine
Top Data Science Influencer by Data Science Salon Magazine in 2020
Premier Speaker on many conferences for 2020 on machine learning