Professional Certificate in Artificial Intelligence and Machine Learning Applications
- 3 Months / Onsite and Online
- Intakes: Every Month
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.
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.
In summary, 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.
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.
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.
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.
Learning Outcomes
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.
Lecture Panel
The lecture panel for the Professional Certificate 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.
Prerequisites
- Basic knowledge in Mathematics (algebra, calculus, statistics).
- Basic knowledge in Programming (preferably Python or another programming language).
Course Outline
| 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. |
This comprehensive certificate program combines theoretical foundations with extensive practical applications in AI and ML. Participants will gain hands-on experience using industry-standard tools like Python, Jupyter Notebooks, and Google Colab, preparing them for careers in data science, AI development, and related fields.
To Whom
This program is designed for individuals across various backgrounds who are eager to develop practical skills in AI/ML disciplines:
Software Professionals in Industry
- Objective: Seeking quicker options to enhance hands-on skills in AI/ML.
- Benefit: Accelerated learning to stay competitive and relevant in the rapidly evolving tech industry.
Undergraduates
- Objective: Integrate AI/ML advancements into their final year research projects.
- Benefit: Gain practical experience and skills applicable to future academic pursuits or career opportunities.
Postgraduates
- Objective: Integrate AI/ML advancements into their final year research projects.
- Benefit: Enhance research outcomes and prepare for advanced career roles in academia or industry.
Other Engineering/Science Professionals
- Objective: Transition into AI/ML fields from backgrounds such as Civil, Mechanical, Electronics, etc.
- Benefit: Acquire new skills and opportunities for career advancement in high-demand sectors.
OL/AL Students
- Objective: Prepare for future careers in AI/ML and related fields.
- Benefit: Early exposure and preparation for educational pathways and career opportunities in AI/ML.
Method of Delivery
The delivery method for this certification is entirely online, requiring candidates to have access to a personal computer.
Medium of Instruction
English or Sinhala and simple English
Evaluation Method
To earn this professional certification, candidates are required to successfully complete a three-part examination process, comprising an assignment, a written examination, and an online practical examination.
Course Duration
Lectures - 90 hours
Self Assessments - XX hours
Course Schedule
6 Months | 2 Days per week | 7:30PM - 9:30PM
Course Fee
Per Participant Fee: Rs X0,000/=
Payment Method: Full payment (Rs. 10,000/= discount for full payment) or Rs. X0,000/= X 3 installments
- 1st Payment when registering the course
- 2nd Payment one month after the 1st payment
- 3rd payment one month after the 2nd payment
Refund Policy
Students requesting refunds must email their request to finance.it@gsdc.lk. Refunds are only available to those who have paid the full class fees; fees paid in installments are non-refundable. Refund eligibility is as follows:
Applicants who have not attended the course for more than one week during the first month are entitled to a 90% refund and enrollment cancellation.
Applicants who have attended the first month of the course and not attended for more than one week during the second month are entitled to a 70% refund and enrollment cancellation.
Applicants who have attended the first two months of the course and not attended for more than one week during the third month are entitled to a 50% refund and enrollment cancellation.
No refunds will be issued after three months from the start of the course. Additionally, the course fee is non-transferable under any circumstances.
How to Apply?
- You Apply
Tell us a little about yourself and we’ll help with the rest. Our convenient online application tool only takes 10 minutes to complete.
- We Connect
After you submit your application, an admissions representative will contact you and will help you to complete the process.
- You Get Ready
Once you’ve completed your application and connected with an admissions representative, you’re ready to create your schedule.