Artificial Intelligence (Machine Learning & NSA Mode)
Course Overview
Explore the world of Artificial Intelligence through the lens of Machine Learning and NSA (Neural Symbolic AI) Mode. This course introduces core AI concepts, teaching you how machines learn from data to make predictions, automate tasks, and uncover insights. You'll gain hands-on experience with machine learning models, algorithms, and neural-symbolic integration techniques — equipping you with the skills to develop intelligent systems that combine the power of data-driven learning with symbolic reasoning.
This module covers the basics of Artificial Intelligence (AI), introducing concepts like machine learning and neural networks. Participants will learn how AI algorithms work, focusing on the key components of deep learning and its applications in modern technologies. The module provides foundational knowledge of AI tools and techniques, preparing participants for more advanced roles in AI development and implementation.
This Course is Specifically Designed to Provide Participants With:
- Gain an in-depth knowledge of what artificial intelligence is, its historical evolution, and its various applications in today's world
- Understand the basics of machine learning, including key concepts, algorithms, and practical applications
- Learn about the structure and functioning of neural networks, and explore deep learning architectures such as CNNs and RNNs
- Get familiar with popular tools and libraries used in AI and machine learning, and apply them through practical, real-world projects
- Discuss the ethical implications of AI, including bias, privacy, and the impact on employment
Course Curriculum
Definition and History
- What is AI?
- The history of AI: Key Milestones
- Turing Test & its significance
- Narrow AI vs. General AI
- Strong AI vs. Weak AI
Applications of AI
- AI in healthcare, finance, education, and more
- Case studies and real-world examples
Ethical Considerations
- Bias in AI
- The role of AI in job displacement
- Privacy concerns and AI regulations
Basic Concepts
- What is machine learning?
- Types of machine learning: supervised, unsupervised, and reinforcement learning
- The ML pipeline: data collection, preprocessing, model training, evaluation, and deployment
Supervised Learning
- Regression vs. classification
- Common algorithms: linear regression, logistic regression, decision trees, and support vector machines
Unsupervised Learning
- Clustering vs. Association
- Common algorithms: k-means, hierarchical clustering, and apriori algorithm
Reinforcement Learning
- Key concepts: agents, environments, and rewards
- Exploration vs. exploitation
- E-learning and policy gradients
Tools and Libraries
- Introduction to popular ML tools: scikit-learn, TensorFlow, and PyTorch
- Practical examples and hands-on projects
Basics of Neural Networks
- Biological inspiration: the human brain
- Structure of a neural network: neurons, layers, and activation functions
- Forward propagation and backpropagation
Deep Learning Architectures
- Introduction to deep learning
- Convolutional neural networks (CNNs) and their applications in image processing
- Recurrent neural networks (RNNs) and their applications in sequence data
- Generative adversarial networks (GANs) and their use cases
Training and Optimization
- Gradient descent and its variants
- Overfitting and regularization techniques
- Hyperparameter tuning and model validation
Technologies and Tools You'll Learn
- Scikit-learn - Machine learning library for Python
- TensorFlow - Open-source platform for machine learning
- PyTorch - Deep learning framework
- Neural Networks - Building and training neural networks
- CNNs - Convolutional Neural Networks for image processing
- RNNs - Recurrent Neural Networks for sequence data
- GANs - Generative Adversarial Networks
- Neural Symbolic AI (NSA) - Combining neural networks with symbolic reasoning
Who Should Take This Course?
- Software developers looking to transition into AI and machine learning
- Data scientists wanting to deepen their ML knowledge
- IT professionals interested in AI development
- Students pursuing careers in artificial intelligence
- Business professionals wanting to understand AI capabilities
- Anyone interested in building intelligent systems
Key Benefits
- Comprehensive AI Knowledge - Gain in-depth understanding of AI, ML, and neural networks
- Hands-On Experience - Work with real-world projects using TensorFlow, PyTorch, and scikit-learn
- Deep Learning Expertise - Master CNNs, RNNs, and GANs for various applications
- Neural Symbolic AI - Learn to combine data-driven learning with symbolic reasoning
- Career Advancement - Prepare for advanced roles in AI development and implementation
- Ethical AI Understanding - Learn about AI ethics, bias, and responsible AI development
Prerequisites
Basic programming knowledge (preferably Python) is recommended. Familiarity with mathematics (linear algebra, statistics) would be beneficial but not required. This course is designed for beginners in AI/ML, though some programming experience will help you get the most out of the hands-on projects.
Certification
Upon successful completion of this course, you will receive a certificate of completion from TEC - Telecommunication Engineering College. This certificate validates your understanding of Artificial Intelligence, Machine Learning, and Neural Networks, preparing you for advanced roles in AI development.
The course includes hands-on projects and assessments to ensure you have practical experience with AI tools and techniques.
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Ready to Explore the World of AI?
Enroll in Machine Learning & NSA Mode Course Today!
Contact Us for Course Information
enquiry@tec.edu.my