AI & ML Engineering Professional Certificate
Course provided by Model Institute of Engineering & Technology
5 modules
Explore the fundamentals of Artificial Intelligence & Machine Learning
4-4.5 Level NCrF
National Credit Framework
320 Hours
Flexible Learning
Intermediate Level
Certificate Diploma Course
09-10 Credits
Course Overview
This program blends Microsoft’s Azure-based AI curriculum with MIER’s hands-on training, delivering practical skills for creating region-specific AI solutions in Jammu’s smart city and tourism sectors. Students gain real-world experience in MIER’s AI Lab, working with Azure Machine Learning, Python, and reinforcement learning to build intelligent troubleshooting agents and scalable ML pipelines. Through skill-focused workshops on Azure AI workflows, model optimization, and autonomous agent development, learners are equipped for career paths such as AI Solutions Developer and Machine Learning Engineer.
Key Learning Highlights
Hands-on AI development using Azure ML, Python, and reinforcement learning
Practical application of AI to smart city and tourism sector challenges in Jammu
Design and deployment of intelligent troubleshooting agents and ML pipelines
Skill-focused workshops on Azure AI workflows and model optimization
Building autonomous AI agents for real-world problem solving
Career preparation for roles like AI Solutions Developer and Machine Learning Engineer
Tools & Platform Used
Learning Outcome
By the end of this course, students will be able to:
Develop and deploy AI solutions using Azure ML, Python, and reinforcement learning
Design and implement intelligent troubleshooting agents and scalable ML pipelines
Apply AI techniques to region-specific challenges in smart city and tourism sectors
Optimize AI models and workflows using Azure AI best practices
Build and manage autonomous AI agents for real-world applications
Demonstrate industry-ready skills for roles such as AI Solutions Developer and Machine Learning Engineer
Master the course with just 5 Modules
This course takes students from the fundamentals of AI and machine learning to advanced techniques in model optimization and cloud deployment. Beginning with core concepts, data engineering, and neural networks, students progress to hands-on development using TensorFlow, Scikit-learn, and Azure ML. The journey concludes with real-world deployment strategies, ethical considerations, and a capstone project delivering industry-ready AI/ML solutions.
Fundamentals of AI and Machine Learning
Learn core concepts of AI/ML, including supervised & unsupervised learning, regression, classification, and clustering.
Understand neural networks, data preprocessing, and feature engineering basics.
Explore ethical AI principles and real-world applications.
Data Engineering for AI/ML
Perform data ingestion, cleaning, and transformation using Python, Pandas, and NumPy.
Apply feature engineering techniques for better model performance.
Build scalable data pipelines with Azure Data Factory and SQL integration.
Building and Optimizing ML Models
Develop ML models using Scikit-learn, TensorFlow, and Azure ML.
Apply hyperparameter tuning and evaluate models with metrics like ROC and AUC.
Explore ensemble methods and fundamentals of deep learning.
Deploying AI/ML Solutions
Deploy models using Azure ML pipelines, Kubernetes, and Docker.
Implement real-time and batch inference, with monitoring and retraining workflows.
Study deployment case studies in sectors like healthcare and finance.
Capstone Project
Design and build an end-to-end AI/ML application using Azure ML and Python frameworks.
Integrate data pipelines, model training, and deployment in a real-world scenario.
Solve industry-relevant challenges through a practical, hands-on project.
Roles
- Machine Learning Engineer
- AI Engineer (Azure)
- ML Operations Associate (MLOps)
- Data Science Engineer
- Cloud AI Solutions Developer
- AI DevOps Engineer
