Description
Certification Name: Certificate in Artificial Intelligence (AI) Engineer
Course Id: CEAP/Q0001.
Eligibility: Graduation or Equivalent.
Objective: The Certified Artificial Intelligence (AI) Engineer course is designed to equip professionals with the knowledge and skills to design, develop, and deploy AI-based solutions across various industries. The course covers core AI concepts, machine learning algorithms, deep learning, natural language processing (NLP), computer vision, data preprocessing, model training, and deployment.
Duration: Three Month.
How to Enroll and Get Certified in Your Chosen Course:
Step 1: Choose the course you wish to get certified in.
Step 2: Click on the “Enroll Now” button.
Step 3: Proceed with the enrollment process.
Step 4: Enter your billing details and continue to course fee payment.
Step 5: You will be redirected to the payment gateway. Pay the course and exam fee using one of the following methods:
Debit/Credit Card, Wallet, Paytm, Net Banking, UPI, or Google Pay.
Step 6: After successful payment, you will receive your study material login ID and password via email within 48 hours of fee payment.
Step 7: Once you complete the course, take the online examination.
Step 8: Upon passing the examination, you will receive:
• A soft copy (scanned) of your certificate via email within 7 days of examination.
• A hard copy (original with official seal and signature) sent to your address within 45 day of declaration of result.
Step 9: After certification, you will be offered job opportunities aligned with your area of interest.
Online Examination Detail:
Duration- 60 minutes.
No. of Questions- 30. (Multiple Choice Questions).
Maximum Marks- 100, Passing Marks- 40%.
There is no negative marking in this module.
| Marking System: | ||||||
| S.No. | No. of Questions | Marks Each Question | Total Marks | |||
| 1 | 10 | 5 | 50 | |||
| 2 | 5 | 4 | 20 | |||
| 3 | 5 | 3 | 15 | |||
| 4 | 5 | 2 | 10 | |||
| 5 | 5 | 1 | 5 | |||
| 30 | 100 | |||||
| How Students will be Graded: | ||||||
| S.No. | Marks | Grade | ||||
| 1 | 91-100 | O (Outstanding) | ||||
| 2 | 81-90 | A+ (Excellent) | ||||
| 3 | 71-80 | A (Very Good) | ||||
| 4 | 61-70 | B (Good) | ||||
| 5 | 51-60 | C (Average) | ||||
| 6 | 40-50 | P (Pass) | ||||
| 7 | 0-40 | F (Fail) | ||||
Key Benefits of Certification- Earning a professional certification not only validates your skills but also enhances your employability. Here are the major benefits you gain:
Practical, Job-Ready Skills – Our certifications are designed to equip you with real-world, hands-on skills that match current industry demands — helping you become employment-ready from day one.
Lifetime Validity – Your certification is valid for a lifetime — no renewals or expirations. It serves as a permanent proof of your skills and training.
Lifetime Certificate Verification – Employers and institutions can verify your certification anytime through a secure and reliable verification system — adding credibility to your qualifications.
Industry-Aligned Certification –All certifications are developed in consultation with industry experts to ensure that what you learn is current, relevant, and aligned with market needs.
Preferred by Employers – Candidates from ISO-certified institutes are often prioritized by recruiters due to their exposure to standardized, high-quality training.
Free Job Assistance Based on Your Career Interests – Receive personalized job assistance and career guidance in your preferred domain, helping you land the right role faster.
Assessment Modules:
Module 1 – Foundations of AI & Programming: Introduction to artificial intelligence and historical developments, Mathematical foundations (linear algebra, probability, statistics, calculus), Programming for AI (Python basics, data types, functions, libraries), Data structures & algorithms relevant to AI, Data engineering for AI: data acquisition, cleaning, pipelines, Exploratory data analysis and visualization
Module 2 – Machine Learning Algorithms & Techniques: Supervised learning (regression, classification, decision trees, SVMs), Unsupervised learning (clustering, dimensionality reduction, PCA), Ensemble methods and boosting (Random Forest, XGBoost, bagging), Model evaluation, validation and hyper‑parameter tuning, Feature engineering, selection and extraction, Handling imbalanced data, over‑fitting, under‑fitting
Module 3 – Deep Learning & Neural Networks: Basic neural network architecture (layers, activation functions, loss, backpropagation), Convolutional Neural Networks (CNNs) for image data, Recurrent Neural Networks (RNNs), LSTM/GRU for sequential data, Transfer learning and pre‑trained models, Generative models (GANs, autoencoders)
Module 4 – Natural Language Processing, Computer Vision & Multimodal AI: Text preprocessing, tokenisation, embeddings (Word2Vec, GloVe, BERT etc.), Sequence‑to‑sequence models, attention mechanisms and transformers, Computer vision tasks: image classification, object detection, segmentation, Multimodal AI: combining vision, text, audio, Use‑cases and applications in real‑world domains
Module 5 – AI Deployment, MLOps & Cloud Integration: Model deployment strategies (APIs, microservices, edge, mobile), MLOps lifecycle: model versioning, monitoring, governance, Continuous integration/continuous deployment for AI models, Cloud platforms for AI (AWS, Azure, GCP) and serverless AI services, Scalability, performance optimisation, inference latency and cost considerations, Ethical AI and bias mitigation in deployed systems
Module 6 – Emerging Trends, Strategy & Ethical Considerations in AI: Reinforcement learning and autonomous systems, Foundation models and large language models (LLMs), AI for IoT, edge computing, and robotics, AI governance, regulation and responsible AI (bias, fairness, privacy), Business strategy for AI: road‑map, value realisation, change management, Future of AI: quantum AI, explainable AI (XAI), sustainability and social impact
Job Opportunities in Artificial Intelligence (AI)
Professionals design, develop, and deploy AI and intelligent systems to solve business problems across IT services, BFSI, healthcare, e-commerce, automotive, telecom, and smart city sectors.
Top Roles: AI Engineer/Developer, Machine Learning Engineer, Data Scientist (AI Focus), AI Research Engineer, NLP Engineer, Computer Vision Engineer, AI Product Developer/Architect, AI Consultant/Specialist, Robotics & Automation Engineer (AI), Freelance AI Developer/Entrepreneur
Key Skills: Machine learning, deep learning, NLP, computer vision, data preprocessing, model deployment, AI system architecture, cloud AI services, reinforcement learning, AI product development, AI strategy & consultancy
Salary Range (India):
- Entry Level: ₹6–7 LPA
- Mid Level: ₹7–20 LPA
- Senior Level: ₹20–25 LPA+
- Freelance/Project-based: ₹10–35+ LPA
Industries: IT services & product companies, BFSI, healthcare & pharmaceuticals, retail & e-commerce, logistics & automotive, telecom & media, smart cities
Scope: Career growth includes AI research, solution architecture, product development, AI consultancy, robotics, generative AI, AIoT, autonomous systems, and leadership roles such as Head of AI, with global remote opportunities.
