AI 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
160 Hours
Flexible Learning
Intermediate Level
No prior experience required
Certificate Diploma Course
06-08 Credits
Course Overview
Blends IBM’s advanced Coursera curriculum with MIER’s hands-on labs, applying AI to regional challenges like agricultural analytics in Jammu and Kashmir.
Practical training in MIER’s AI lab using Scikit-learn, PyTorch, and Apache Spark for scaling ML models and deploying deep learning architectures.
Skill-focused workshops on neural network design, big data ML pipelines, and capstone projects, preparing for roles like Machine Learning Engineer.
Key Learning Highlights
Hands-on AI Development with Scikit-learn, PyTorch, and Apache Spark
Real-world Applications including agricultural analytics for Jammu & Kashmir
Neural Network Design and deployment of deep learning architectures
Big Data ML Pipelines for building and scaling AI solutions
Capstone Projects simulating industry workflows from concept to deployment
Career-Ready Skills for roles such as Machine Learning Engineer, AI Engineer, and Data Scientist
Tools & Platform Used
Learning Outcome
By the end of this course, students will be able to:
Understand core concepts of Generative AI, embeddings, vector databases, and AI agent architectures
Build AI-powered applications using HTML, CSS, JavaScript, Python, LangChain, and OpenAI APIs
Integrate open-source LLMs and vector databases for advanced AI functionalities
Deploy ethical, secure, and scalable AI solutions with AI safety and bias mitigation practices
Execute industry-aligned capstone projects to address real-world challenges and drive AI innovation
Master the course with just 5 Modules
This course takes students from the foundations of AI engineering to the deployment of real-world AI applications. Starting with generative AI, embeddings, vector databases, and AI agents, students gain hands-on experience with HTML, CSS, JavaScript, Python, LangChain, and OpenAI APIs. They advance to open-source LLMs, semantic search, and ethical deployment, culminating in a capstone project that delivers a complete, industry-ready AI solution.
Fundamentals of AI Engineering
Understand generative AI, LLMs, embeddings, vector databases, and AI agents.
Explore AI safety principles and bias mitigation strategies.
Study real-world industry use cases and applications.
Developing AI Applications
Build applications using HTML, CSS, JavaScript, and Python.
Integrate OpenAI APIs for chatbots, travel agents, and recommendation systems.
Apply frontend and backend skills to AI-driven solutions.
Advanced AI with LangChain and Open-Source Models
Work with open-source LLMs like LLaMA and AI orchestration via LangChain.
Implement semantic search using vector databases.
Develop intelligent AI agents with tool integration.
AI Deployment and Safety
Ensure secure API integration and scalable deployments.
Apply ethical AI practices and bias mitigation.
Monitor and maintain AI applications with best practices.
Capstone Project
Design and build a complete AI-powered application.
Use LangChain, OpenAI APIs, and open-source models in an end-to-end workflow.
Address a real-world problem through innovative AI solutions.
Roles
- Machine Learning Engineer
- AI Engineer (Azure)
- ML Operations Associate (MLOps)
- Data Science Engineer
- Cloud AI Solutions Developer
- AI DevOps Engineer
