Tunas AI
*This program is for after SPM
This program is dedicated for school SPM leavers which covers awareness and general understanding on AI and cultivate systematic problem solving skills using AI tools.
Industry visit, problem-based project etc
- 16 Modules
- 40 Days
Data
Acuisition
Data
Cleaning
Exploratory
Data Analysis
Data
Pre-Processing
Model
Development
Performance
Evaluation
Model
Deployment
The Modules
Pre-Requisite
Preparation
- Python for Machine Learning
Learn the fundamentals of Python programming, focusing on libraries like NumPy, Pandas, and Matplotlib. This course provides essential coding skills for building machine learning models, covering data manipulation, basic algorithms, and visualization techniques.
- Git Management
Understand the basics of version control using Git. Learn to track changes in your projects, collaborate with others, and manage code efficiently through repositories like GitHub, enabling smooth project development and teamwork.
- Introduction to AI
Discover the core concepts of Artificial Intelligence. This course provides an overview of AI technologies, their real-world applications, and how AI is transforming industries. Explore key topics such as supervised and unsupervised learning, and AI ethics.
- Responsible AI
Delve into the principles of ethical AI development. This course covers fairness, transparency, and accountability in AI systems, ensuring that your AI projects adhere to ethical standards while minimizing unintended bias or negative societal impacts.
Beginner
Data
- Data Science Basics
Explore the fundamentals of data science, including data collection, cleaning, analysis, and visualization. Learn essential tools and techniques to turn raw data into valuable insights using Python.
- Database System
Get introduced to database systems, including relational databases and SQL. Learn how to design, query, and manage databases effectively, ensuring efficient data storage and retrieval for machine learning applications.
- Data Structure for ML
Understand how to structure data for machine learning tasks. This course covers key data structures such as arrays, linked lists, trees, and graphs, focusing on optimizing data processing and storage for ML algorithms.
- Deep Learning
Dive into the world of deep learning, exploring neural networks and their architectures. Learn how to build, train, and optimize deep learning models using frameworks like TensorFlow or PyTorch to solve complex tasks.
Intermediate
Model Development
- Computer Vision
Learn the basics of computer vision, from image processing to building models for tasks like object detection, image classification, and facial recognition. This course covers popular tools such as OpenCV and TensorFlow for vision applications.
- Time Series Forecasting
Master techniques for forecasting future trends based on time-dependent data. This course teaches methods like ARIMA, exponential smoothing, and LSTM networks to model and predict stock prices, sales, and other temporal patterns.
- Natural Language Processing
Explore techniques for processing and analyzing human language using AI. Learn about text preprocessing, sentiment analysis, and language models like BERT and GPT to build chatbots, translators, and other NLP applications.
- Generative AI
Discover the power of generative models, such as GANs and VAEs, to create new data from existing datasets. This course explores applications of generative AI in areas like image synthesis, music generation, and data augmentation.
Advanced
Model Deployment
- Cloud Computing & MLOps
Learn how to deploy AI models in cloud environments. This course covers the fundamentals of cloud computing, CI/CD pipelines, and MLOps, ensuring efficient and scalable model deployment for production.
- AI System Deployment
Understand the complete lifecycle of deploying AI systems, from model training to real-time inference. This course focuses on deployment strategies, monitoring, and maintaining AI systems to ensure performance and scalability.
- Edge Computing
Explore the concept of edge computing, where AI models are deployed on local devices rather than centralized servers. Learn about low-latency applications, IoT integration, and optimizing AI for resource-constrained environments.
- Projects
Apply everything you’ve learned by working on hands-on projects. This capstone experience involves developing and deploying AI models for real-world problems, reinforcing skills in data handling, model building, and deployment.
Expected Outcomes
- Comprehensive understanding on AI
- Systematic problem-solving skills with AI using no-code AI tools
- Ethical and responsible AI usage