Introduction to AI
Explore the fundamentals of AI—without the math—in this introductory course.
Target Audience:
Students, professionals, and anyone curious about artificial intelligence who wants to understand AI concepts without diving into complex mathematics.
Course Overview:
This course provides a comprehensive introduction to artificial intelligence concepts and applications, focusing on practical understanding rather than mathematical complexity. Topics include:
- Understanding what AI is and how it impacts our daily lives
- Exploring different types of AI systems and their capabilities
- Fundamental concepts of machine learning and neural networks
- Real-world applications of AI across various industries
- Ethical considerations and future trends in AI
- Hands-on experience with AI tools and platforms
By the end of this course, learners will understand:
- Core AI concepts and terminology
- How machine learning and deep learning work
- Different types of AI applications and use cases
- Basic principles of natural language processing
- Computer vision fundamentals
- AI ethics and responsible AI development
- How to evaluate AI systems and their limitations
The course runs for 8 weeks (2 hours per week) and uses intuitive, no-code platforms for exercises - no programming experience required. Perfect for beginners looking to understand AI concepts in an accessible, practical way.
Introduction to Deep Learning
Target Audience: Students, professionals, and educators interested in deep learning concepts and applications.
This course provides an overview of deep learning fundamentals on modern Intel® architecture. Topics include:
- Types of problems that can be solved with deep learning
- Understanding neural networks and their building blocks
- Fundamentals of building and training deep learning models
- Exploring essential neural network architectures
By the end of this course, learners will have practical knowledge of:
- Fundamental neural network architectures: feedforward, convolutional, and recurrent networks
- Concepts like gradient descent, backpropagation, and activation functions
- Choosing appropriate architectures, tuning hyperparameters, and validating models
- Using pretrained models for transfer learning
The course runs for 12 weeks (3 hours per week) and uses Python for exercises - prior experience is helpful but not required.
Introduction to Machine Learning
Duration: 12 Weeks | Cost: Kshs 5,000
Target Audience: Students, professionals, and educators interested in machine learning concepts and applications.
This course provides an overview of machine learning fundamentals on modern Intel® architecture. Topics include:
- Types of problems that can be solved with ML
- Understanding key building blocks
- Fundamentals of building models in machine learning
- Exploring essential algorithms
By the end of this course, learners will have practical knowledge of:
- Supervised learning algorithms
- Concepts like underfitting, overfitting, regularization, and cross-validation
- Choosing algorithms, tuning parameters, and validating models
The course runs for 12 weeks (3 hours per week) and uses Python for exercises — prior experience is helpful but not required.