Diploma in AI and Machine Learning Engineering
Course provided by Model Institute of Engineering & Technology
5 modules
Explore the fundamentals of Artificial Intelligence & Machine Learning
5-5.5 Level NCrF
National Credit Framework
480 Hours
Flexible Learning
Beginner Level
No prior experience required
Diploma Program
18 Credits
Course Overview
This course provides a practical, application-focused pathway into AI and machine learning, guiding learners from core concepts to advanced deployment techniques. Students gain hands-on experience with Python, Scikit-learn, Pandas, Matplotlib, and Azure ML Studio to design, train, and deploy ML models. With a focus on real-world challenges in education, healthcare, and agriculture, the program includes industry-aligned projects with local MSMEs, preparing graduates for roles such as Machine Learning Engineer, Data Analyst, and MLOps Associate.
Key Learning Highlights
Build and deploy ML pipelines using Python, Scikit-learn, and Azure ML.
Gain hands-on experience in feature engineering, model tuning, and MLOps.
Apply AI/ML to region-specific challenges in sectors like education, healthcare, and agriculture.
Work on industry-aligned capstone projects with local MSMEs.
Develop employable skills for Data Analyst and Machine Learning Engineer roles.
Tools & Platform Used
Learning Outcome
By the end of this course, students will be able to:
Implement end-to-end ML pipelines using Python and Scikit-learn
Apply feature engineering and model optimization techniques to improve performance
Deploy ML models as APIs using Azure ML and basic MLOps workflows
Analyze and solve region-specific problems in sectors like education, healthcare, and agriculture
Collaborate with local industry (MSMEs) to design and deliver practical AI/ML solutions
Build a portfolio showcasing skills for roles such as Machine Learning Engineer and Data Analyst
Master the course with just 6 Modules
This course takes learners from the fundamentals of AI and machine learning to advanced model deployment and MLOps practices. Beginning with core concepts, data analysis in Python, and Scikit-learn workflows, students progress to hands-on development of complete ML pipelines. The journey concludes with Azure-based deployment, industry collaborations, and a capstone project addressing real-world challenges in education, healthcare, and agriculture.
Introduction to AI and ML
Learn the history, taxonomy, and evolution of AI and ML.
Understand classical ML vs deep learning and types of learning.
Explore applications in regional contexts like education, healthcare, and agriculture.
Python for Data Analysis
Work with NumPy, Pandas, Matplotlib, and Seaborn for data handling and visualization.
Perform data cleaning and exploratory data analysis (EDA).
Prepare datasets for ML workflows.
ML with Scikit-learn
Implement regression, classification, and clustering algorithms.
Evaluate models using standard metrics and validation techniques.
Create ML pipelines for efficient model training and deployment.
ML Deployment with Azure ML & MLOps
Deploy ML models on Azure ML and generate REST APIs.
Integrate CI/CD workflows for continuous deployment.
Monitor and maintain models post-deployment.
Feature Engineering and Model Tuning
Handle missing values, scaling, and encoding for better model performance.
Apply hyperparameter tuning using GridSearch and RandomSearch.
Optimize ML workflows for accuracy and efficiency.
Capstone Project with Local MSMEs
Work on a complete ML pipeline addressing a real-world regional challenge.
Collaborate with MSMEs in sectors like agriculture, healthcare, or logistics.
Present and validate the solution with academic and industry feedback.
Roles
- Data Analyst
- Junior Data Scientist
- Machine Learning Engineer
- MLOps Engineer
- AI Solutions Developer
