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Certificate Program in GenAI

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Advanced Certificate Program in Gen AI

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Professional Certificate Program

4,785 Rating

Generative AI refers to artificial intelligence systems designed to create new content, such as text, images, music, or videos, based on patterns learned from existing data. 

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Gen AI Course Overview

Covers foundational AI models, prompt engineering, and AI-powered applications.

Course Key Features

Role plays
AI Model Basics

"AI Model Basics" refers to the foundational concepts and principles behind artificial intelligence models, including how they are designed, trained, and applied to solve specific problems.

Peer feedback
Prompt Engineering

Prompt engineering is the practice of designing and refining input prompts to effectively communicate with AI models, guiding them to generate desired outputs.

Personal brand
AI for Automation

AI for automation refers to the use of artificial intelligence technologies to perform tasks or processes without human intervention.

Workshop
Hands-on AI Applications

Hands-on AI applications refer to practical, real-world uses of artificial intelligence where individuals actively engage with AI tools or systems

Program Fee
₹
23,600.00

EMI Facility Available on Checkout.

  • You will be allocated the cohort post successful payment of your fee. You may check the details of our upcoming cohorts in our Timetable section or please wait for your email on this topic once you have completed the payment.
Program syllabus
Download Syllabus

Skills you'll gain

AI model training, Prompt tuning, AI application development

Skills
OpenAI APIs
LLMs
NLP
Python

Gen AI Course Advantage

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Category Details
Total Duration 6 Hours
Online/Live Sessions 2 Hours
Pre Recorded Lectures 1 Hour
Self Study Material 1 Hour
Assignments 1 Count
Assessments 1 Hour

1. History & Evolution of AI

2. AI vs Traditional Programming

3. Applications of AI in Different Industries

4. Types of AI (Narrow AI, General AI, Super AI)

1.  AI Frameworks: TensorFlow, PyTorch, Hugging Face

2. Cloud AI Platforms: AWS, Azure, GCP

3. AI Hardware: GPUs, TPUs, Edge AI

4. Ethical Considerations in AI

5. AI Regulations & Governance

1. What is Generative AI?

2. How GenAI Works: Data, Models, Training

3. Evolution of GenAI (GANs to GPT-4, DALL·E, Sora)

4. Key Applications of GenAI (Content, Images, Code)

5. Limitations & Risks of GenAI

4. Ethical Considerations in AI

5. AI Regulations & Governance

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CategoryDetails
Total Duration7 Hours
Online/Live Sessions2 Hours
Pre Recorded Lectures2 Hour
Self Study Material1 Hour
Assignments1 Count
Assessments1 Hour

1. Introduction to Supervised Learning

2. Regression vs Classification

3. Common Algorithms: Decision Trees, SVM, Neural Networks

4. Overfitting & Underfitting

5. Model Evaluation Metrics (Accuracy, Precision, Recall)

1. Introduction to Clustering

2. K-Means & Hierarchical Clustering

3. Dimensionality Reduction (PCA, t-SNE)

4. Anomaly Detection Techniques

5. Use Cases of Unsupervised Learning

1. Introduction to RL: Agents, States, Rewards

2. Q-Learning & Deep Q Networks

3. Policy-Based Learning

4. RL in Robotics & Gaming

5. Challenges & Future of RL

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CategoryDetails
Total Duration8 Hours
Online/Live Sessions3 Hours
Pre Recorded Lectures2 Hour
Self Study Material1 Hour
Assignments1 Count
Assessments1 Hour

1. Basics of Neural Networks

2. Feedforward & Backpropagation

3. Activation Functions (ReLU, Sigmoid, Softmax)

4. Hyperparameter Tuning

5. Optimizers (SGD, Adam, RMSprop)

1. Introduction to CNNs

2. Convolution, Pooling, Fully Connected Layers

3. Transfer Learning & Pretrained Models

4. Image Classification using CNNs

5. Object Detection & Segmentation

1.Introduction to RNNs & LSTMs

2. Sequence Modeling with RNNs

3. Introduction to Transformers

4. Self-Attention Mechanism

5. Comparing CNNs, RNNs, and Transformers

1.Introduction to RNNs & LSTMs

2. Sequence Modeling with RNNs

3. Introduction to Transformers

4. Self-Attention Mechanism

5. Comparing CNNs, RNNs, and Transformers

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CategoryDetails
Total Duration10 Hours
Online/Live Sessions3 Hours
Pre Recorded Lectures2 Hour
Self Study Material2 Hour
Assignments2 Count
Assessments2 Hour

 1: Introduction to GANs

2. Architecture: Generator & Discriminator

3. Types of GANs (DCGAN, CycleGAN, StyleGAN)

4. Training & Challenges in GANs

5. Applications of GANs

 1: Introduction to VAEs

2. Encoder-Decoder Architecture

3. Difference Between GANs & VAEs

4. Applications of VAEs in Image & Text

5. Improving VAEs for Real-World Applications

 1: Introduction to Diffusion Models

2. Training Process of Diffusion Models

3. Applications in Image & Video Generation

4. Large-Scale GenAI Model Advancements Applications

5. Future of Generative AI

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CategoryDetails
Total Duration9 Hours
Online/Live Sessions3 Hours
Pre Recorded Lectures2 Hour
Self Study Material1 Hour
Assignments2 Count
Assessments2 Hour

 1.Introduction to NLP

2. Text Processing & Tokenization

3. Word Embeddings (Word2Vec, GloVe)

4. Named Entity Recognition & POS Tagging

5. Sentiment Analysis & Text Classification

1. Introduction to Transformers for NLP
2. BERT & its Applications
3. GPT & Evolution to GPT-4
4. Text Summarization & Translation using T5
5. Fine-Tuning Transformer Models
1. Introduction to Text Generation
2. ChatGPT & Conversational AI
3. Building AI-Powered Chatbots
4. AI-Generated Content (Blogs, Emails)
5. Ethical Concerns in AI-Generated Text
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CategoryDetails
Total Duration8 Hours
Online/Live Sessions3 Hours
Pre Recorded Lectures2 Hour
Self Study Material1 Hour
Assignments1 Count
Assessments1 Hour

1. Introduction to Text-to-Image Models

2. DALL·E & Stable Diffusion

3. Fine-Tuning Image Generation Models

4. Bias & Ethical Concerns in AI-Generated Images

5. Applications in Design & Art

1. Introduction to AI in Audio Processing.

2. AI-Based Music & Voice Generation

3. AI Video Generation (Runway ML, Sora)

4. Deepfakes & Their Ethical Implications.

5. AI in Media & Entertainment

1. Text-to-Speech &Speech-to-Text AI

2. Multimodal AI Models (CLIP, Flamingo)

3. AI for Accessibility (Speech-to-Braille)

4. AI-Based Content Personalization

5. Future Trends in Multimodal AI

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CategoryDetails
Total Duration8 Hours
Online/Live Sessions3 Hours
Pre Recorded Lectures2 Hour
Self Study Material1 Hour
Assignments1 Count
Assessments1 Hour

1. Deployment Strategies for AI Models

2. Using APIs for AI Services

3. Model Hosting on Cloud Platforms

4. Optimizing AI for Performance & Cost

5. Monitoring & Maintaining AI Models

1. AI Bias & Fairness
2. Explainability & Transparency in AI
3. AI Regulations & Responsible AI
4. Privacy & Data Security in AI
5. Case Studies of Ethical AI Failures
1. Scaling AI Models for Enterprises
2. Distributed Computing for AI
3. Edge AI vs Cloud AI
4. AI in Production: MLOps
5. Future of Scalable AI
    • Lesson 1: Project Selection & Proposal
CategoryDetails
Total Duration4 Hours
Online/Live Sessions0 Hours
Pre Recorded Lectures1 Hour
Self Study Material1 Hour
Assignments1 Count
Assessments1 Hour

2. Dataset Collection & Preprocessing

3. Model Selection & Training

4. Model Deployment & Testing

5. Final Presentation & Evaluation

Benefits of the Gen AI Course

Nova Benefits
Certificate
Eligibility
Faculty Profile

Get certified to gain exciting career opportunities

Add the certification to your resume and open doors to new opportunities in the roles of:

  • GenAI Developer/ Junior AI Engineer
  • AI Prompt Engineer
  • AI Solutions Associate
  • Data Analyst(AI/ML Focus)
  • AI Technical Support Engineer
 Certificate

🎓 Eligibility & Selection Criteria

➤ Student must have passed or is pursuing last semester of the Qualified Degree program that is being pursued.

➤ Candidates who completed these programs within the past year are also eligible, provided they meet all other criteria.

➤ Students who have passed these courses in less than One Year are also eligible to join the program within the criteria defined.

✅ Qualified Degree Programs:

  • B.Tech
  • B.E
  • M.C.A.
  • M.Sc (Computer Applications) or equivalent

All candidates must go through the defined selection process and meet the criteria to enroll in the program.

Note: A face-to-face interview may be required (not mandatory for all).

⭐ Course Review Summary

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Average Rating
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Gen AI Architect & Large Language Model Specialist

Profile Summary: A pioneering AI researcher with 10+ years in NLP and generative models, including 4 years focused exclusively on LLMs (GPT, Claude, Gemini). Currently leads GenAI adoption at a Tier-1 IT services firm. Combines hands-on experience in model fine-tuning with strategic insights for enterprise deployment. Holds a PhD in Computational Linguistics and multiple patents in AI-generated content verification.

Key Roles and Achievements:

  • Transformer architectures & prompt engineering
  • RAG pipeline optimization
  • Ethical AI guardrails
Applied AI Solutions Lead (Industry Practitioner)

Profile Summary: A hands-on AI product builder with 7+ years implementing GenAI solutions across healthcare (diagnostic chatbots) and finance (automated report generation). Former Microsoft AI MVP specializing in Azure OpenAI Service integrations. Focuses on bridging theoretical concepts with real-world business use cases through sandbox projects.

Key Roles and Achievements:

  • Multi-agent AI systems
  • Fine-tuning for domain specificity (legal/medical)
  • Cost-performance trade-off analysis
Gen AI Programming & Deployment Expert

Profile Summary: A hands-on GenAI engineer with 6+ years specializing in production-grade LLM deployments. Currently leads AI integration at a global SaaS company, optimizing models for latency, cost, and accuracy. Deep expertise in LangChain, LlamaIndex, and vector DBs. Creator of multiple open-source tools for model quantization and GPU optimization. Passionate about teaching the full stack—from prototyping to scaling GenAI solutions.

Key Roles and Achievements:

  • LLM orchestration frameworks
  • Model quantization & GPU optimization
  • Real-time inference pipelines