Artificial Intelligence
Introduction to modern AI concepts, programming foundations, machine learning concepts, and practical AI applications.
Course overview
Artificial Intelligence introduces you to the ideas and tools behind modern AI — from programming foundations to machine learning and practical AI applications.
You will learn the Python and data skills AI work depends on, understand how machine learning models learn from data, build and evaluate models with scikit-learn, and get hands-on with neural networks, natural language processing, computer vision and generative AI.
The focus is practical: every concept is paired with a notebook or mini project, and the course closes with a responsible-AI discussion and a capstone AI application.
Course at a glance
What you will learn
- Explain key AI concepts: AI, machine learning, deep learning and generative AI
- Use Python, NumPy and Pandas to prepare data
- Understand the maths intuition behind machine learning
- Build regression and classification models with scikit-learn
- Evaluate and improve models with the right metrics
- Apply clustering and dimensionality reduction
- Build and train simple neural networks
- Work with text (NLP) and images (computer vision)
- Use large language models and AI APIs in applications
- Understand responsible and ethical use of AI
Course curriculum
Module 1: Introduction to Artificial Intelligence4 lessons
Module 2: Python Foundations for AI4 lessons
Module 3: Maths Essentials for Machine Learning3 lessons
Module 4: Machine Learning Fundamentals4 lessons
Module 5: Regression & Classification4 lessons
Module 6: Model Evaluation & Improvement4 lessons
Module 7: Unsupervised Learning3 lessons
Module 8: Neural Networks & Deep Learning4 lessons
Module 9: Natural Language Processing3 lessons
Module 10: Computer Vision Basics3 lessons
Module 11: Generative AI & Large Language Models4 lessons
Module 12: Responsible AI & Capstone3 lessons
Practical projects
A regression model that predicts prices from property features.
A text classification model that separates spam from genuine messages.
Group customers by behaviour using clustering.
A neural network that recognises image categories using transfer learning.
A practical assistant built on a large language model API.
Tools & software
Who is this course for?
- Students curious about AI and machine learning
- Graduates who want an introduction to AI
- Working professionals who want to apply AI in their work
- Developers adding machine learning to their skills
- Career changers exploring AI
Career applications
The skills from this course can be applied to roles and projects such as:
- Foundation for Machine Learning Engineer and Data Scientist paths
- Adding AI features to software products
- Automating tasks with AI tools and APIs
- Data analysis and prediction projects
- AI-assisted workflows in any profession
Requirements
- A laptop or desktop computer with internet access
- Basic Python is helpful — the course includes a Python-for-AI refresher
- School-level mathematics; the required maths is explained intuitively
- Google Colab can be used if your computer is not powerful
Certificate
Certificate of Completion
On successfully completing the course requirements — lessons, practical projects/assignments and assessments — you receive a Vector Tech Academy Certificate of Completion. Every certificate carries a unique certificate ID and a QR code that anyone (a client, an employer or an institute) can verify on our website. The certificate recognises that you completed this course and its projects; it is not a university degree or a government-recognised qualification.
Frequently asked questions
Do I need to be good at maths for the AI course?
Do I need a powerful computer?
Will this course make me an AI expert?
Ready to start Artificial Intelligence?
Build skills, create projects and earn a verifiable certificate.
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