Postgraduate Diploma in Artificial Intelligence and Machine Learning Applications

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  • Intakes: Jan, Apr, Jul, Oct

The demand for intelligent application development is rapidly increasing worldwide, including in Sri Lanka. However, several observations highlight specific challenges within the Sri Lankan education industry. while the demand for AI/ML skills is growing in Sri Lanka, there is a clear need for accessible and practical educational pathways, professional training programs, and governmental support to facilitate smoother transitions and capitalize on the opportunities presented by the evolving digital landscape.

Lack of Practical Certificate/Diploma Courses

There is a notable absence of authorized certificate or diploma programs that cater to individuals seeking hands-on experience rather than lengthy theoretical courses.

Shortage of Professional Assistance Programs

There is a need for short-term professional assistance programs focused on AI/ML topics, especially for professionals looking to transition careers.

Transition of Software Engineers to AI/ML

Many software engineers are shifting their focus to AI/ML domains due to promising career prospects and opportunities for growth.

Interest from Non-IT Professionals

Professionals from diverse backgrounds such as Civil, Mechanical, and Electronics are interested in transitioning to AI/ML fields, driven by the prospects of higher salaries, remote work opportunities, and industry demand.

Government and Industry Focus

The Sri Lankan government, through initiatives like FITIS, emphasizes leveraging AI and machine learning for process optimization across various sectors.

Foreign Investment and Employment

Foreign application development firms are increasingly driving AI/ML projects and employing Sri Lankan talent due to competitive labor costs, which also helps mitigate economic challenges like currency fluctuations.

Course Description

This course provides a comprehensive introduction to the foundational concepts and practical applications of Artificial Intelligence (AI) and Machine Learning (ML). Aimed at beginners and intermediate learners, it covers a broad spectrum of topics essential for understanding the current landscape and future directions of AI/ML technologies.

Throughout the course, emphasis will be placed on practical applications and real-world case studies across industries such as healthcare, finance, retail, and autonomous vehicles. By the end of the course, students will have gained a solid foundation in AI/ML principles and techniques, empowering them to apply these skills effectively in their careers or further academic pursuits.

Fundamental Knowledge of AI/ML Advancements and Applications

Students will begin by mastering the basic definitions and distinctions between AI, machine learning, and deep learning. They will delve into the historical evolution of these technologies, from their inception to modern breakthroughs. Emphasis will be placed on understanding the key milestones and driving factors that have shaped the field over the years.

Data Cleansing and Feature Selection Strategies

Participants will learn essential techniques for ensuring data quality and preprocessing, including handling missing data, outlier detection, and normalization. They will develop skills in feature engineering, the process of selecting and transforming raw data into meaningful features that enhance model performance. Feature selection techniques, such as correlation analysis and Recursive Feature Elimination (RFE), will be explored to optimize model inputs effectively.

Supervised and Unsupervised Learning Applications

The course will cover supervised learning algorithms like linear regression, logistic regression, decision trees, and support vector machines. Students will understand the principles behind these methods and gain hands-on experience in building predictive models using real-world datasets. Unsupervised learning techniques, including clustering (e.g., K-means, hierarchical clustering) and dimensionality reduction (e.g., PCA, t-SNE), will also be studied for their applications in pattern recognition and data exploration.

Developing Deep Learning-Based Solutions

Participants will explore the fundamentals of neural networks, including basic architectures and their applications in various domains. Advanced topics such as Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data analysis will be covered in depth. Hands-on sessions using popular deep learning frameworks such as TensorFlow, Keras, and PyTorch will enable students to implement and fine-tune deep learning models for tasks like image recognition, natural language processing, and predictive analytics.

Fundamental Knowledge of AI/ML Advancements and Applications
  • Understand Basic Concepts: Learn the definitions and differences between AI, machine learning, and deep learning.
  • Historical Context and Evolution: Explore the history and evolution of AI and ML technologies.
  • Current Trends and Future Directions: Stay informed about current advancements, trends, and future directions in AI/ML.
  • Applications Across Industries: Understand how AI/ML is applied in various industries such as healthcare, finance, retail, and autonomous vehicles.
  • Data Quality and Preprocessing: Learn techniques for handling missing data, outliers, and ensuring data quality.
  • Feature Engineering: Develop skills to create meaningful features from raw data.
  • Feature Selection Techniques: Understand and apply methods like correlation analysis, mutual information, and advanced techniques like Recursive Feature Elimination (RFE).
  • Supervised Learning: Learn to build models using algorithms like linear regression, logistic regression, decision trees, and support vector machines.
  • Unsupervised Learning: Understand clustering (K-means, hierarchical clustering) and dimensionality reduction techniques (PCA, t-SNE).
  • Model Evaluation and Tuning: Master techniques for evaluating model performance (e.g., accuracy, precision, recall) and tuning hyperparameters.
  • Neural Networks Basics: Understand the structure and functioning of neural networks.
  • Advanced Architectures: Learn about Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.
  • Frameworks and Tools: Gain hands-on experience with deep learning frameworks like TensorFlow, Keras, and PyTorch.
  • Real-World Applications: Implement deep learning models for real-world problems such as image recognition, natural language processing, and predictive analytics.
  • Fundamental Knowledge of AI/ML Advancements and Applications: 
    • Understand Basic Concepts: Learn the definitions and differences between AI, machine learning, and deep learning.
    • Historical Context and Evolution: Explore the history and evolution of AI and ML technologies.
    • Current Trends and Future Directions: Stay informed about current advancements, trends, and future directions in AI/ML.
    • Applications Across Industries: Understand how AI/ML is applied in various industries such as healthcare, finance, retail, and autonomous vehicles.
  • Data Cleansing and Feature Selection Strategies: 
    • Data Quality and Preprocessing: Learn techniques for handling missing data, outliers, and ensuring data quality.
    • Feature Engineering: Develop skills to create meaningful features from raw data.
    • Feature Selection Techniques: Understand and apply methods like correlation analysis, mutual information, and advanced techniques like Recursive Feature Elimination (RFE).
  • Supervised and Unsupervised Learning Applications:
    • Supervised Learning: Learn to build models using algorithms like linear regression, logistic regression, decision trees, and support vector machines.
    • Unsupervised Learning: Understand clustering (K-means, hierarchical clustering) and dimensionality reduction techniques (PCA, t-SNE).
    • Model Evaluation and Tuning: Master techniques for evaluating model performance (e.g., accuracy, precision, recall) and tuning hyperparameters.
  • Develop Deep Learning-Based Solutions: 
    • Neural Networks Basics: Understand the structure and functioning of neural networks.
    • Advanced Architectures: Learn about Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.
    • Frameworks and Tools: Gain hands-on experience with deep learning frameworks like TensorFlow, Keras, and PyTorch.
    • Real-World Applications: Implement deep learning models for real-world problems such as image recognition, natural language processing, and predictive analytics.
  • Bachelor’s degree or equivalent prior learning experience. 
  • Basic programming and mathematical skills (or a foundation course if required).
  • Completion of NVQF level 7 may also be considered equivalent.
  • Bachelor’s degree holders in Computer Science, Information Technology, Engineering, Mathematics, Physics, or Statistics. 
  • Professionals in business, management, or marketing who want to leverage AI/ML for decision-making, automation, and innovation. 
  • Graduates with a keen interest in pursuing advanced research in AI/ML. 
  • Non-technical professionals (e.g., healthcare, finance, or social sciences) interested in applying AI/ML in their industries. 
  • Innovators and founders who aim to create AI-driven products or services.  

The lecture panel for the Postgraduate Diploma in Artificial Intelligence and Machine Learning Applications represents a unique blend of practical industry expertise and academic proficiency. Each member brings over 20 years of experience in information technology, offering a wealth of specialized skills and knowledge derived from extensive hands-on practice in the field. Their backgrounds span various sectors within the IT industry, ensuring students gain a comprehensive understanding of real-world challenges and effective solutions.

Module 1: Flavors of Data Science
Introduction to Artificial Intelligence.
Overview of Deep Learning.
Introduction to Machine Learning.
Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning.
Introduction to Fuzzy Systems.
Knowledge Modelling and Ontologies.
Module 2: Setting Up Environment
Installing Python and necessary libraries.
Configuring Jupyter Notebooks.
Overview of Google Colab.
CPU Vs GPU for machine learning tasks.
Module 3: Data Analysis Overview
Finding and accessing datasets.
Data cleansing techniques.
Understanding Overfitting vs. Underfitting.
Module 4: Data Analysis Overview – II
Handling noise in predictions.
Principles of cross-validation.
Performance metrics: Precision, Recall, F-Measure.
Module 5: Supervised Learning
Types of Regression.
Hands-on exercises using Google Colab.
Module 6: Supervised Learning
Logistic Regression types.
Practical implementation using Google Colab.
Module 7: Supervised Learning
Bayes Theorem and Naïve Bayes Classifier.
Practical experiments.
Module 8: Supervised Learning
Support Vector Machines (SVM).
Hyperparameter tuning for SVM.
Hands-on experiments.
Module 9: Supervised Learning
Decision Trees.
 Hands-on experiments.
Module 10: Supervised Learning
Random Forest.
Hands-on experiments.
Module 11: Supervised Learning
K-Nearest Neighbors (KNN).
Hands-on experiments.
Module 12: Supervised Learning
Introduction to Artificial Neural Networks (ANN).
Understanding biases and activation functions.
Module 13: Gradient Descent
Concept and hands-on experiments.
Module 14: Deep Learning Basics
Introduction to Back-propagation algorithm.
Module 15: Supervised Learning
Advanced Artificial Neural Networks.
Practical exercises.
Module 16: Real-World Use Cases
Discussion on applications in various industries.
Module 17: Unsupervised Learning
Elbow Technique for K-Means Clustering.
Module 18: Unsupervised Learning
K-Means Clustering in depth.
Hands-on experiments.
Module 19: Revision Session
Module 21 – 24: Capstone Project and Presentations
Collaborative project applying AI/ML techniques to solve a real-world problem.
Presentation of findings and outcomes.

The delivery method for this certification is entirely online, requiring candidates to have access to a personal computer.

Sinhala and Simple English

To earn this post graduate diploma, candidates are required to successfully complete a three-part examination process, comprising an assignment, a written examination, and an online practical examination.

12 months
Rs 250,000/=

How to Apply

  • You Apply

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  • We Connect

After you submit your application, an admissions representative will contact you and will help you to complete the process.

  • You Get Ready

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How To Apply

Your Application

Tell us a little about yourself and we’ll help with the rest. Our convenient online application tool only takes 10 minutes to complete.

Our Response

After you submit your application, an admissions representative will contact you and will help you to complete the process.

Your Journey

Once you’ve completed your application and connected with an admissions representative, you’re ready to create your schedule.

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