What You'll Learn
Six core competency areas — from AI fundamentals to cutting-edge generative models
AI & ML Fundamentals
Core AI concepts, types of learning, and machine learning algorithms including supervised, unsupervised, and reinforcement learning.
FoundationDeep Learning
Neural networks, CNNs, RNNs — built and trained using TensorFlow and PyTorch on real datasets with live projects.
Core SkillAdvanced NLP
Transformers, BERT, GPT, word embeddings, and language model fine-tuning — applied to real text classification and generation tasks.
In-DemandGenerative AI & LLMs
GANs, VAEs, GPT & Gemini deep dives, prompt engineering, and fine-tuning large language models for real use cases.
Cutting EdgeAI Ethics & Deployment
Model fairness, explainability (XAI), responsible AI, and hands-on deployment pipelines using REST APIs and cloud platforms.
Production ReadyComputer Vision & NLP Applications
Object detection (YOLO, Faster R-CNN), image segmentation, sentiment analysis, and building end-to-end AI applications.
Applied AIBuilt to Get You Hired in AI
A program that combines rigorous theory with hands-on practice — so you graduate with both a portfolio and a certificate.
Unlock the power of artificial intelligence with our industry-designed program, built for beginners aiming to become practising AI engineers. You'll start from the very foundations — understanding what AI, ML, and deep learning really are — and progressively build toward fine-tuning large language models and deploying AI systems in production.
Every module is taught by mentors who've built AI systems at scale in the industry. You'll work on 8+ live, mentor-reviewed projects that go directly into your portfolio — proving your skills to every employer you meet. The course ends with a performance-based LOR, dual certification, and direct placement referrals.
Course Benefits
Course Curriculum
Four stages, twelve modules, one pipeline to becoming an AI engineer — click any module to expand
- AI vs ML vs Deep Learning
- Types of AI systems
- Industry applications
- Challenges & future trends
- ANN architecture
- Forward & backpropagation
- Loss functions & optimizers
- Hands-on implementation
- TensorFlow & PyTorch
- Model building pipelines
- Key tools & ecosystem
- Real use cases
- CNN architecture deep dive
- Transfer learning
- ResNet vs VGG
- Vision projects
- YOLO vs Faster R-CNN
- Image segmentation
- U-Net architecture
- Detection projects
- Text preprocessing
- Feature extraction
- Word2Vec, GloVe, FastText
- Applied NLP tasks
- RNNs, LSTMs, GRUs
- Transformer architecture
- BERT & GPT overview
- Advanced NLP projects
- Markov Decision Process
- Q-Learning algorithm
- Deep Q-Networks (DQN)
- RL game agents
- GANs & VAEs
- GPT, Gemini, Claude
- Fine-tuning & prompt engineering
- Build GenAI apps
- Model deployment via APIs
- AI bias & fairness
- Explainability (XAI)
- Responsible AI practices
- Edge AI & TinyML
- AI on IoT devices
- AI in finance & healthcare
- Business impact strategy
- AI for robotics & automation
- AI in gaming (agents)
- AI regulations & policies
- Capstone project
Capstone Project · Dual Certification · Placement Ready
Every stage feeds into a portfolio-ready AI engineer — backed by 8+ live projects and a performance-based LOR.
Your Dual Certificates
Two industry-recognized credentials awarded on successful completion
What Makes This Course Special
Everything you need to go from curious learner to confident AI engineer
Industry Expert Trainers
Learn from MNC-certified professionals who've built real AI systems — not just academics.
Hands-On Projects
Build 8+ real AI applications that go straight into your portfolio and impress every employer.
Dual Certification
Course Completion + Internship Certificate — two industry-recognized credentials in one program.
Performance-Based LOR
A star-rated, personalized Letter of Recommendation based on what you actually built and achieved.
1:1 Mentor Support
Direct access to mentors for doubt clearing, code reviews, and career guidance whenever you need it.
Placement Referrals
Direct referrals to our hiring partner network — Amazon, Google, Infosys, TCS, and 50+ more.
Requirements
- Basic programming knowledge — Python preferred but not mandatory
- Foundational understanding of maths & statistics (we'll fill the gaps)
- A computer with internet connection
- Curiosity, commitment, and hunger to build with AI
Material Includes
- 35+ hours of recorded video lectures
- Lifetime LMS access — revisit anytime
- Section quizzes, assessments & assignments
- 8+ industry-based hands-on projects
- Course Completion Certificate
- Internship Experience Certificate
Simple, Transparent Pricing
Pick the learning style that suits you best — both include dual certification and placement support