About this course
Build advanced skills in artificial intelligence and deep learning with the NxT Certified Advanced AI Engineer & Deep Learning Architect program. Over one year, explore neural networks, natural language processing, computer vision, and transformer architectures while developing the practical skills to create and deploy sophisticated AI solutions.
The program covers the end-to-end AI development lifecycle, from data preparation and model training to performance optimization, MLOps, and cloud deployment.
- Design, train, and evaluate deep learning models.
- Apply NLP, computer vision, and transformer techniques to real-world problems.
- Improve model performance and plan scalable AI deployments.
- Build MLOps workflows for reliable model delivery and monitoring.
Duration: 1 year
Program fee: XX,XXX
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
- Design and train neural networks and deep learning models for varied problem types.
- Apply natural language processing methods to text classification, language understanding, and generation tasks.
- Develop computer vision solutions for image analysis and recognition.
- Explain and apply transformer architectures to appropriate AI use cases.
- Prepare data, select evaluation metrics, and assess model performance.
- Optimize models for accuracy, efficiency, and deployment constraints.
- Build MLOps workflows for model versioning, testing, deployment, and monitoring.
- Deploy AI solutions using cloud platforms and scalable infrastructure.
Projects you will build
Build and evaluate a deep learning model for a classification or forecasting problem, documenting data preparation, model choices, and results.
Develop an NLP application such as a text classifier, document information extractor, or question-answering prototype.
Create a computer vision solution for an image recognition or object detection use case.
Fine-tune or adapt a transformer model for a defined language task and compare its performance with a baseline.
Implement an MLOps pipeline that packages, tests, deploys, and monitors a model.
Complete a capstone project that takes an AI solution from problem definition through cloud deployment, with attention to scalability and performance.
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 with a background in computer science, engineering, mathematics, data science, or a related field. Familiarity with programming, preferably Python, and foundational knowledge of machine learning and mathematics is recommended. Applicants should be prepared to work with datasets, code, and model development tools.
What next after this course
Questions students ask
The program duration is one year.
Topics include neural networks, deep learning, natural language processing, computer vision, transformer architectures, model optimization, MLOps, and cloud deployment.
A foundation in programming, preferably Python, along with basic machine learning and mathematics knowledge is recommended. Related study or experience in computer science, engineering, mathematics, or data science can be helpful.
Yes. Assignments include deep learning, NLP, computer vision, transformer, and MLOps projects, culminating in an end-to-end AI capstone.
Yes. The curriculum includes MLOps workflows and the deployment of AI solutions on cloud platforms.
The listed program fee is 149000.