This course offers a comprehensive introduction to Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), with a strong emphasis on practical applications. Through structured modules, learners will build a foundation in AI by progressing from basic concepts to the implementation of AI models.
Key topics include:
- Optimization techniques
- Linear regression
- Deep learning architectures
- Real-world problem-solving strategies
The course integrates low-code/no-code and hands-on coding exercises using interactive dashboards to reinforce theoretical knowledge and ensure practical skills development.
By the end of the course, learners will be able to:
- Understand the roles and relationships between AI, ML, and DL, and their contributions to modern technologies.
- Evaluate the impact of dataset specification and model selection on AI system performance and decision-making.
- Apply optimization techniques such as brute-force search, gradient-free methods, and gradient descent.
- Construct and refine linear regression models using batch, mini-batch, and stochastic gradient descent.
- Analyze error functions to assess model accuracy.
- Identify and apply major deep learning architectures and activation functions, including ReLU, sigmoid, and softmax.
- Explain and experiment with Generative Adversarial Networks (GANs) and Diffusion Models in the context of image generation, text completion, and other creative AI applications.
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Module 1 Generic Recipe for AI-Related Problems
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Module 2 Visual Introduction to Optimization
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Module 3 Visual Introduction to Machine Learning through Linear Regression
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Module 4 Visual Introduction to Deep Learning
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Module 5
Part 1: Overview and Evolution of Language Models
Part 2: Sequence Analysis – Neural Network-Based Approaches
Part 3: Large Language Models (LLMs) – Quick Overview
- Module 6
Part 1: Introduction to Generative AI (GenAI) Part I
Part 2: Introduction to Generative AI (GenAI) Part II
- Low-code / no-code platforms
- Interactive dashboards
- Python (optional)
- Jupyter Notebooks
This course is intended for:
- Students and professionals beginning their journey into AI
- Learners seeking to develop applied skills in machine learning and deep learning
- Those interested in generative models and large language models without diving too deeply into advanced mathematics
The course combines visual explanations with interactive content to support diverse learning styles. Exercises are designed to be beginner-friendly while laying the foundation for more advanced study in AI-related fields.