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Certificate in AI Developer

Job Description

To train professionals in developing and deploying AI-powered applications using modern frameworks and best practices for scalable solutions.


Objective: The Certified AI Developer course is designed to equip professionals with the skills and knowledge to design, develop, and deploy artificial intelligence applications using modern AI techniques and tools. The course covers foundational concepts of AI, including machine learning, deep learning, natural language processing (NLP), computer vision, and reinforcement learning.

Eligibility Criteria

Criteria 1

Criteria 2

Experience

Training Qualification

12th

Pursuing Continuous Schooling

1 Year

None

12th

Passed

6 Months

None

Degree

Passed

No Experience

None


Sector

Artificial Intelligence

Certifying Bodies

National Education Training And Development

Type of Organisation

Board of Vocational Education

Learning Module In Job Role/Syllabus

Occupation Standards/Syllabus

NCO Code

Mandatory/ Optional

Duration

  • Module 1: Introduction to Artificial Intelligence

  • Module 2: Programming for AI Development

  • Module 3: Machine Learning Fundamentals

  • Module 4: Deep Learning and Neural Networks

  • Module 5: Natural Language Processing (NLP) and Computer Vision

  • Module 6: AI Deployment and Maintenance





N/A





Mandatory





3 Months


Career Opportunities

  • AI Developer / AI Engineer

  • Machine Learning Engineer (AI-Focused)

  • AI Research Scientist

  • Natural Language Processing (NLP) Engineer

  • Computer Vision Engineer

  • AI Consultant / Solution Architect

  • AI Product

  • Freelance / Contract AI Developer


Duration Hours

Theory :

100

Practical :

100

Employability Skills :

20

OJT (Mandatory) :

20

Specializations in Course

  • Python, R, and ML frameworks (TensorFlow, PyTorch, scikit-learn)

  • Machine learning algorithms and deep learning

  • Natural Language Processing (NLP) and Computer Vision

  • AI model deployment and optimization

  • Data pre-processing, feature engineering, and predictive modelling

  • AI project implementation in real-world applications


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