Authors: Dr. Jarapala Ramesh Babu, Dr. Praveen Gugulothu, Dr. Azmeera Anitha Kumari, Dr. Prabhu G Benakop, Mr. Srikanth Renikunta
ISBN-13: 9789349554689
DOI: https://doi.org/10.5281/zenodo.20365667
Format: Paperback and e-book
Pages: 377
Product Dimensions: 6 x 9 inch
Publisher: Bharat Global Publications
Dr. Jarapala Ramesh Babu is an academician and researcher in the field of Electrical and Electronics Engineering, with a focus on electric vehicles, power systems, power electronics, and renewable energy technologies. He is currently working as an Assistant Professor at Methodist College of Engineering and Technology, Hyderabad. He completed his Master of Engineering from Osmania University and Ph.D. at Biju Patnaik University of Technology (BPUT), Odisha. His research work primarily centers on hybrid electric vehicles, energy management systems, and sustainable energy solutions. Dr. Ramesh Babu has contributed to several research publications in reputed journals and conferences, particularly in areas related to renewable energy integration and electric vehicle technologies. He is also actively involved in innovation and has contributed to multiple patents in engineering applications. He continues to contribute to teaching, research, and technological development, with a strong emphasis on sustainable and energy-efficient engineering solutions.
Dr. Praveen Gugulothu is a dedicated academician and researcher in the field of Computer Science and Engineering, with expertise in machine learning, deep learning, soft computing, and big data. He earned his Ph.D. from the National Institute of Technology (NIT), Warangal, focusing on deep learning applications in biological data analysis. He has over 14 years of teaching experience and currently serves as Vice Principal at Siddhartha Institute of Technology & Sciences (SITS), Hyderabad. The institute, established in 2008, is a UGC-autonomous engineering college affiliated with JNTU Hyderabad, known for its emphasis on quality technical education and industry-oriented learning. Dr. Praveen has taught subjects such as Artificial Intelligence and Machine Learning and has published several research papers in reputed international journals. His research interests include healthcare applications of deep learning, data analysis, and intelligent systems. He continues to contribute to academia through teaching, research, and academic leadership, guiding students and aspiring professionals in the field of data science and emerging technologies.
Dr. Azmeera Anitha Kumari is an academician and researcher with expertise spanning Mechanical Engineering. She serves as an Assistant Professor and Head of the Department, demonstrating strong commitment to teaching, research, and academic leadership. Head of the Department of Mechanical Engineering at Siddhartha Institute of Technology & Sciences (SITS), Hyderabad. The institute, established in 2008, is a UGC-autonomous engineering college affiliated with JNTU Hyderabad, known for its emphasis on quality technical education and industry-oriented learning. Her research interests include Artificial Intelligence, Machine Learning, computational mechanics, and advanced engineering applications. She has contributed to several research publications, particularly in areas such as material analysis, modeling, and intelligent systems that bridge both computing and mechanical domains. Dr. Azmeera Anitha Kumari is dedicated to fostering critical thinking, problem-solving skills, and innovation among students. She emphasizes practical learning through modern tools, laboratories, and emerging technologies, preparing students to excel in both industry and research.
Dr. Prabhu G Benakop, Principal, Department of Electrical and Electronics Engineering at Methodist college of engineering and technology, Osmania University Hyderabad.
Mr. Srikanth Renikunta is an Assistant Professor in the Department of Civil Engineering at Methodist College of Engineering and Technology (MCET), Hyderabad. He holds an M.S. in Transportation Engineering from Kansas State University, USA, and a B.E. in Civil Engineering from Vasavi College of Engineering, Hyderabad. With over 15 years of teaching and research experience, including a stint at Jigjiga University, Ethiopia, he is currently pursuing his PhD at CSIR-Central Road Research Institute (CRRI), New Delhi. His work spans transportation engineering, road safety, sustainable buildings, electric mobility, and the application of AI and machine learning in engineering. In addition, Srikanth is a certified Road Safety Auditor, and an IGBC Accredited Professional. He is passionate about responsible AI, sustainability, and bridging academia with real-world applications.
Machine learning has moved far beyond the boundaries of academic research and is now an essential part of everyday technology. Whether it’s recommending what to watch next, recognizing speech, detecting fraud, or supporting medical decisions, machine learning is quietly shaping many aspects of our lives. As data becomes increasingly central to decision-making, the ability to understand and work with it is more valuable than ever.
This book, Machine Learning with Python: A Practical Guide for Data Scientists, is written to help you get comfortable with both the ideas and the practice of machine learning. Rather than focusing only on theory, it aims to show how these concepts are applied in real situations. The goal is to help you build a solid understanding while also giving you the confidence to work on your own projects.
Python has become a popular choice for machine learning because it is easy to learn and comes with a wide range of powerful libraries. In this book, you’ll use tools like NumPy, pandas, and scikit-learn to explore data, build models, and evaluate results. The emphasis is on learning by doing, with practical examples that you can follow and adapt.
The chapters are arranged to guide you step by step. You’ll start with the basics—understanding data, preparing it, and visualizing it—before moving on to different types of learning techniques and how to measure their performance. Each section is designed to build on what came before, so you can progress at a comfortable pace.
This book is intended for students, beginners, and professionals who want to develop a strong foundation in machine learning. Some familiarity with Python and basic statistics will be helpful, but everything is explained in a straightforward way to keep it accessible.
More than anything, this book encourages curiosity and hands-on exploration. Machine learning is a constantly evolving field, and the best way to grow in it is by experimenting, asking questions, and learning from experience.