A publication company from 1997 announces the launch of an upcoming book from Dr.Ganapathi Pulipaka – ‘A Greater Foundation for Machine Learning Engineering:
The Hallmarks of the Great Beyond in PyTorch, TensorFlow, R, and Python.’ Data is the lifeblood of all the business. In
today’s world, the data-driven decisions are exponentially leading the
enterprises in a competitive landscape. Machine learning has been highly
successful in rolling out industrial applications in manufacturing, energy,
utilities, life sciences, and finance industries. The evolution of machine
learning powered by GPUs and CPUs has led the revolution of computers to learn without explicitly being programmed in making the machines think like humans. Enterprises are looking to upskill their employees, students from University, and newcomers
from various industries, who are fascinated by the machine learning prowess.Machine learning engineers are looking for a great source of inspiration that
lays a greater foundation filled with innovation and ideas to get the readers a passport of entry point with either little or no coding experience, or any mathematical or statistical background. This book comes with tons of illustrations and data visualizations with neural network architectures,
machine learning algorithms with supervised, unsupervised, and reinforcement learning in PyTorch, TensorFlow, R, and Python.

Machine learning engineering needs a greater foundation of linear algebra and calculus to simply the complex topics such as 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. This book does not hesitate to go into mathematical equations for each machine learning algorithm including bioinspired algorithms for swarm intelligence.

Dr. Ganapathi Pulipaka with his background in Doctor of Computer Science Engineering and Postdoc specialization in machine learning and big data analytics from Colorado University is a top influencer in the machine learning field in America top-rated by Onalytica and Agilience in Data management, Big data, IIoT,Machine learning, Information management is a leading instructor of machine learning bootcamps with Quickstart. Dr. Ganapathi Pulipaka has partnered with world-class Universities for providing training on machine learning such as James Madison University, Florida Atlantic University, Hofstra University,Colorado State University, North Eastern Illinois University, Wagner College,Becker College, University of Texas, and Santa Clara University.

Considering
his teaching experience, research scholar experience, project
implementations experience, he created the book with a greater foundation for
junior data scientists, data analyst, senior data scientists, business
analysts, data engineers, business intelligence analyst, machine learning
engineers, data journalists, data scientists, software engineers, financial
analysts, big data analytics consultants, product analyst, data science
instructor, database administrator, or chief decision

The machine learning engineers, deep learning engineers, and data scientists need blend of machine learning engineering,
algorithms, mathematics, statistics, and coding skills with business
intelligence and analytics tools. Dr. Ganapathi Pulipaka also has another PhD in Business Administration, data analytics, and enterprise resource management from California University to provide the greater foundation needed in data engineering for Azure with Azure HDInsight, Amazon ecosystem, Hadoop, Apache Spark, cloud computing, predictive analytics, Power BI, R, and Python. This book not only helps to launch the careers of data scientists, but also aids the machine learning engineers as a reference guide along with certifications they take from Quickstart machine learning bootcamps providing sufficient pre-requisites and literature to ramp up onto the certifications and successfully launch their career into machine learning engineering.

You
can contact him on Twitter, Instagram, LinkedIn, and Facebook page or send a review on the book at his email that can be included on the book.

A Greater Foundation for Machine Learning Engineering – The Hallmarks of the Great Beyond in PyTorch

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