About this course
The NxT Certified AI & Machine Learning Developer program is a six-year course designed to build practical skills in data analysis, machine learning, and applied AI. Learn to work with Python, SQL, statistics, and data processing while developing a strong foundation in predictive modelling.
Apply your skills to practical business problems and create projects such as sales forecasts, recommendation engines, lead-scoring systems, and customer behaviour predictions.
- Build programming and data skills with Python and SQL.
- Explore statistics, data preparation, and machine learning algorithms.
- Develop predictive models and evaluate their performance.
- Apply AI and machine learning methods to business-focused projects.
Tools you will master
Hands-on practice on the same software that companies use every day.
Google GeminiAI
GitHub CopilotAI
VS CodeAI
GitHubAI
ClaudeAI
PerplexityAI
NotebookLMAI
What you will study, module by module
- AI Basics and Everyday Uses
- Generative AI: How It Works
- Prompt Engineering Foundations
- AI for Study and Workplace Tasks
- Fact-Checking and Reliable Research
- Privacy, Safety and Responsible Use
- Integrated Practice and Review
- AI Pair Programming Setup: VS Code and GitHub Copilot
- AI-Assisted Front-End Development
- Python Coding and AI Agents in the Editor and Terminal
- GitHub Team Workflow and AI Code Review
- Calling AI APIs from Your Own App
- AI-Assisted Testing, Debugging and Documentation
- AI-Assisted Deployment and Final Project
After this course you will be able to
- Write Python code for data analysis and machine learning tasks.
- Use SQL to query and work with structured data.
- Apply statistical concepts to understand datasets and support modelling decisions.
- Clean, transform, and prepare data for analysis and predictive modelling.
- Understand and apply core machine learning algorithms.
- Build and evaluate predictive models for business problems.
- Develop practical applications such as sales forecasting, recommendation engines, and lead scoring.
- Analyse customer behaviour data to identify patterns and support predictions.
Projects you will build
Sales forecasting project: prepare historical sales data, build a forecasting model, and interpret its results.
Recommendation engine: use user or product data to generate relevant recommendations.
Lead-scoring model: analyse lead attributes and develop a model to estimate conversion likelihood.
Customer behaviour prediction: use customer data to identify behavioural patterns and predict selected outcomes.
Data preparation assignment: clean, transform, and document a dataset using Python and SQL.
Jobs you can get after this course
Who hires for these skills
Earn on your own
- Depending on their skills and portfolio, learners may offer prompt writing, AI-assisted content support or simple no-code workflow setup. Freelance work requires client communication, quality checks and responsible handling of data.
Is this course for you?
Best for
- College students and graduates curious about AI careers.
- Working professionals who want to use AI in their current work.
- Job seekers exploring digital and technology roles.
- Business owners seeking to understand AI-enabled workflows.
- Creative workers and homemakers interested in practical AI tools.
Eligibility
Suitable for learners interested in AI, machine learning, programming, or data analysis. Basic computer literacy is recommended. Prior programming or advanced mathematics experience is not required, though it can be helpful.
What next after this course
Questions students ask
The provided course duration is six years. Confirm the study schedule and program structure with the course provider before enrolling.
The course covers Python, SQL, statistics, data processing, machine learning algorithms, and predictive modelling, with practical AI application projects.
Prior programming experience is helpful but not required. Basic computer literacy and an interest in learning programming and data concepts are recommended.
Project examples include sales forecasting, recommendation engines, lead scoring, customer behaviour prediction, and data preparation.
The skills may support entry-level opportunities in AI development, machine learning, data analysis, and predictive analytics. Employment outcomes depend on your experience, portfolio, and local job requirements.
The listed course fee is XX,XXX. Confirm the currency, payment options, and any additional charges with the course provider.