Dr. Parteek Kumar Bhatia, Associate Professor at the School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA, is a Certified NVIDIA Campus Ambassador, committed to advancing AI, Machine Learning, and Computer Science through research, teaching, and collaboration.
Before joining WSU, he served as a professor at Thapar Institute of Engineering and Technology, Patiala, India, and as a visiting professor at Whitman College, Walla Walla, WA, USA, and the LAMBDA Lab at Tel Aviv University, Israel. Additionally, he held the position of Associate Dean of Student Affairs at Thapar Institute. He earned his doctorate from Thapar Institute and his master’s degree from BITS Pilani, India. He also completed postdoctoral research at Tel Aviv University, Israel.
He works in the area of Machine Learning, Explainable AI, Large Language Models, and AI Applications for Social Good. He is the recipient of the Young Faculty Research Fellowship from the Ministry of Electronics & Information Technology, Govt. of India.
Dr. Kumar has demonstrated excellence in research, securing approximately $246,000 USD in funding from prestigious international and national agencies. He has published more than 100 research papers and articles in reputable journals, conferences, and magazines. He won Gold Medals at the UNL Olympiad II, III, and IV, conducted by the UNDL Foundation in 2013 and 2014.
A well-known author, Dr. Bhatia has published textbooks on machine learning, databases and data mining. His latest book, Machine Learning with Python: Principles and Practical Techniques, was published by Cambridge University Press in 2025. He is also the author of popular books such as Data Mining and Data Warehousing: Principles and Practical Techniques published by Cambridge University Press in 2019, Simplified Approach to DBMS, Simplified Approach to Visual Basic, Simplified Approach to Oracle, and NoSQL in a Day.
Dr. Kumar completed multiple research projects including “Automatic Generation of Sign Language from Hindi Text for Communication and Education of Hearing Impaired People” from DST, India, a Royal Academy of Engineering (UK)-funded research project on “Innovative Research in Pedagogy with Mini-MOOCs Blended with Instruction Strategies to Enhance Quality in Higher Education” as Co-PI. Additionally, he successfully completed a research project on the development of “Indradhanush: An Integrated WordNet for Bengali, Gujarati, Kashmiri, Konkani, Oriya, Punjabi, and Urdu,” sponsored by the Department of Information Technology, Ministry of Communication and Information Technology, Govt. of India.
Passionate about teaching the masses through modern platforms like MOOCs, Dr. Bhatia runs multiple online courses on Udemy with over 45,000 students. He also manages a YouTube channel, “Parteek Bhatia: Simplifying Computer Education,” where he shares video sessions on Machine Learning, Big Data, DBMS, SQL, PL/SQL, and NoSQL.
Books Authored
Machine Learning with Python
This book seamlessly integrates principles with practical application, with each conceptual chapter followed by an implementation chapter for hands-on learning. Major techniques such as regression, classification, clustering, deep learning, and association mining are illustrated with step-by-step coding instructions. Additionally, resources like datasets and code are accessible on GitHub.
Table of Contents
| Machine Learning with Python Preface Acknowledgment Dedication 1. Beginning with Machine Learning 2. Introduction to Python 3. Data Pre-processing 4. Implementing Data Pre-processing in Python 5. Simple Linear Regression 6. Implementing Simple Linear Regression 7. Multiple Linear Regression and Polynomial Linear Regression 8. Implementing Multiple Linear Regression and Polynomial Linear Regression 9. Classification 10. Support Vector Machine Classifier 11. Implementing Classification 12. Clustering 13. Implementing Clustering 14. Association Mining 15. Implementing Association Mining 16. Artificial Neural Network 17. Implementing the Artificial Neural Network 18. Deep Learning and Convolutional Neural Network 19. Implementing Convolutional Neural Network 20. Recurrent Neural Network 21. Implementing Recurrent Neural Network 22. Genetic Algorithm for Machine Learning Reference Index. |
Data Mining and Data Warehousing
Written in lucid language, this valuable textbook brings together fundamental concepts of data mining, machine learning and data warehousing in a single volume. Topics are discussed in detail with their practical implementation using Weka and R language data mining tools.
Table of Contents
Data Mining and Data Warehouse
Preface
Acknowledgments
1 Beginning with Machine Learning
2 Introduction to Data Mining
3 Beginning with Weka and R Language
4 Data Preprocessing
5 Classification
6 Implementing Classification in Weka and R
7 Cluster Analysis
8 Implementing Clustering with Weka and R
9 Association Mining
10 Implementing Association Mining with Weka and R
11 Web Mining and Search Engines
12 Data Warehouse
13 Data Warehouse Schema
14 Online Analytical Processing
15 Big Data and NoSQL
Index
Colour Plates
Simplified Approach to DBMS
One of the best selling book, which explains each and every concept of DBMS in a simplified way. With ample amount of theory and practical questions, it has chapters on SQL and NOSQL as well. Latest topics like data warehousing have also been discussed in detail.
Table of Contents
| Simplified approach to DBMS 1. Fundamentals of Database Management System 2. The architecture of Database Management System 3. Data Models 4. Relational Database Management System 5. Relational Algebra and Calculus 6. Entity-Relationship Model 7. Conversion of ER Diagrams to Tables 8. Normalization for refinement of data 9. Physical Database design 10. Transaction Management 11. Concurrency Control 12. Security and Integrity of data 13. Recovery of data 14. Distributed Database 15. Object-Oriented Databases and expert system 16. DBTG Model 17. Data Warehouse and Data mining 18. No-SQL Database 19. Enterprise Database Products 20. Beginning with SQL 21. Invoking SQL* Plus 22. Performing basic SQL operations 23. Basic SELECT statement 24. Inbuilt functions 25. Grouping of data 26. Joining of tables 27. Sub Queries 28. Managing Tables 29. Database objects, DCL and TCL statements 30. Pl/SQL Fundamentals and its statements 31. Error Handling 32. Cursor Management 33. Subprograms and packages 34. Database triggers 25. Advanced Topics Index |
PL/SQL for beginners
Covers PL/SQL in a simplified manner and specially designed in kindle format to facilitate learning anywhere, any time from smartphone
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NOSQL in a day
It covers NoSQL, Its models, and Big Data in a simplified manner to train learners on advanced concepts quickly and better prepare them for placements and interviews. It is specially designed in kindle format to facilitate learning anywhere, anytime from smartphone.
YouTube Channel
Parteek Bhatia: Simplifying Computer Education
This YouTube channel is a hub for learners passionate about Machine Learning, Big Data, DBMS, SQL, PL/SQL, and NoSQL. With clear, engaging explanations and hands-on coding sessions, the channel simplifies complex topics for students, professionals, and researchers. It features step-by-step tutorials, real-world applications, and practical exercises, making learning accessible and interactive. Whether you’re a beginner or an advanced learner, this channel helps you master key concepts and stay updated with the latest in AI and data science.
From Research to Real-World Impact
Automatic Text to Sign Language Generation System:
This project successfully developed an Indian Sign Language (ISL) generation system from Hindi text, enabling seamless translation through a virtual avatar. Key outcomes include an online multilingual ISL dictionary, a Hindi text parser for ISL processing, a HamNoSys notation system for phonological representation, and web and mobile-based platforms for ISL animation.
For experiencing the online ISL portal, click:
Punjabi WordNet Project
Punjabi WordNet is an online lexical resource. It is more than a conventional Punjabi dictionary. It is a combination of a dictionary and a thesaurus and much more. It provides information about a word from different perspectives with intra-word relationships.
A user can search for a Punjabi word and get its meaning. In addition, it gives the grammatical category namely noun, verb, adjective or adverb for the word being searched. Apart from the category for each sense, the Punjabi WordNet also provides information about the meaning of the word, example of the word usage, its synonyms (words with similar meanings), its antonyms and semantic and lexical relations of the selected word.
Hindi sentiment analysis system
This system provides real-time sentiments of input Hindi sentences. This system performs the sentiment analysis at three levels such as aspect, sentence and document level. At aspect level, the system accepts the input sentence and pre-processes it using Hindi dependency parser. The system extracts its relevant aspects and sentiment-bearing words and generates the dependency graph. Finally, the system assigns the polarity of sentiment word to its corresponding aspect on the basis of minimum path distance.
At the sentence level, the system takes the sentence as input and computes its polarity based on ML algorithms. The system is also able to perform sentiment analysis of tweets. It accepts the tweets on the basis of user-defined hash-tag and computes the overall polarity of hash-tag using machine learning and deep learning algorithms. This is system also has the facility to transliterate the input Hindi text using Google transliteration API.
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Contact Information
📌 Name: Dr. Parteek Kumar
🎓 Designation: Associate Professor (Career Track), School of Electrical Engineering and Computer Science
🏢 Office: EME Building, Room B55
✉️ Email: parteek.kumar@wsu.edu
Feel free to reach out for inquiries, collaborations, or academic discussions!