Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for Business Process Automation teaches how to move beyond simple rule-based automation to processes that read, decide and adapt. You learn where robotic process automation (RPA) fits, then layer intelligence on top: document AI and OCR to handle unstructured inputs, machine learning for classification and decisions, and NLP to process language. The course focuses on identifying which processes are worth automating, designing the workflow, and measuring the return — as well as the change-management and governance that decide whether automation sticks. You finish able to scope and design an intelligent automation solution. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI-driven business process automation — combining RPA, machine learning and document AI to automate workflows and decisions at scale.
📋 Course Objectives
1. Identify high-value processes to automate.
2. Combine RPA with machine learning and document AI.
3. Apply OCR and NLP to unstructured inputs.
4. Design intelligent, adaptive automation workflows.
5. Measure ROI and manage automation governance.
👥 Who Should Enroll?
• Business analysts and process owners
• Automation and RPA developers
• Operations and transformation leads
• Students specialising in enterprise AI
🚀 Key Learning Outcomes
• The ability to scope an intelligent automation project.
• A workflow design combining RPA and AI.
• A practical, ROI-focused automation mindset.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Foundations
Apply linear algebra and calculus concepts to solve AI-related problems • Analyze the role of probability and statistics in machine learning models • Develop a comprehensive understanding of AI and its applications in business process automation
Data Engineering, Preprocessing, and Feature Pipelines
Design and implement data pipelines using Apache Beam and Apache Spark • Evaluate the effectiveness of different data preprocessing techniques for AI models • Configure data quality checks and data validation using Python and Pandas
Model Architecture, Algorithm Design, and Methods
Implement deep learning models using TensorFlow and Keras for business process automation • Analyze the performance of different algorithmic approaches for AI model development • Develop and evaluate the effectiveness of ensemble methods for improved model accuracy
Training, Hyperparameter Optimization, and Evaluation
Configure and train AI models using scikit-learn and Hyperopt for hyperparameter optimization • Evaluate the performance of AI models using metrics such as accuracy, precision, and recall • Develop a comprehensive understanding of cross-validation techniques for model evaluation
Deployment, MLOps, and Production Workflows
Deploy AI models using Docker and Kubernetes for scalable production environments • Design and implement MLOps workflows using Apache Airflow and MLflow • Configure model monitoring and logging using Prometheus and Grafana
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze the ethical implications of AI model development and deployment • Develop strategies for bias mitigation and fairness in AI models • Evaluate the effectiveness of explainability techniques for AI model interpretability
Industry Integration, Business Applications, and Case Studies
Apply AI concepts to real-world business problems and case studies • Evaluate the effectiveness of AI solutions for business process automation • Develop a comprehensive understanding of AI adoption and implementation in various industries
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Apache Beam |
| Covered Tool / Platform | Apache Spark |
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