Master Generative AI & Agentic With Industry Experts
Comprehensive training in Python, R, Statistics, Machine Learning, and Tableau with 3 months intensive training + 4 months LIVE project mentoring.
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Flexible Plans Designed to Fit Your Schedule & Learning Style
Select the learning mode that works best for you — self-paced, live online, or in-person classroom training
Blended Learning
Self Learning + Live Mentoring
- Self Learning + Live Mentoring
- NASSCOM & IBM & AAAI Certification
- 1 Year Access to E-learning
- 25 Capstone & 1 Client Project
- Job Assistance
- 24x7 Learner Assistance
Live Virtual
Instructor Led Live Online
- NASSCOM & IBM & AAAI Certification
- 7-Month | 700 Learning Hours
- 120-Hour Live Online Training
- 25 Capstone & 1 Client Project
- 365 Days Flexi Pass + Cloud Lab
- Placement Assurance
Classroom
In-Person Classroom Training
- NASSCOM & IBM & AAAI Certification
- 7-Month | 700 Learning Hours
- 120-Hour Live Online Training
- 25 Capstone & 1 Client Project
- Cloud Lab Access
- Placement Assurance
Tools & Technologies
Master Industry Tools
Hands-on training on the tools and technologies used by top data teams
worldwide.
Certification
NASSCOM & IBM & AAAI Certification
Flexible Financing Options
Pay in Easy Installments at 0% Interest
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EMI Available
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No Cost EMI & Scholarships Available
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Companies That Hire Our Graduates
200+ leading companies trust Data Sprint graduates to drive their data
initiatives.
Why Data Sprint
Why Thousands Choose Data Sprint
We’re committed to transforming careers with quality education and real-
world experience.
Course Syllabus
Industry-Aligned Curriculum
A structured learning path covering foundations to advanced topics across data science, machine learning, and AI.
Module 1: Foundations
Python & Programming Essentials
• Python fundamentals and advanced concepts
• Object-oriented programming
• Data structures and algorithms
• Error handling and debugging
• Python best practices and PEP standards
• Lab: Python programming challenges
Git Version Control
• Git fundamentals (init, add, commit, push, pull)
• Branching and merging strategies
• Conflict resolution
• GitHub/GitLab workflows
• Pull requests and code reviews
• Git best practices for collaboration
• Lab: Collaborative project with Git workflow
REST APIs Development
• REST architecture principles
• HTTP methods (GET, POST, PUT, DELETE, PATCH)
• Status codes and error handling
• Authentication and authorization (JWT, OAuth)
• API documentation with Swagger/Open API
Flask Framework:
• Flask basics and routing
• Request/response handling
• Flask extensions (Flask-RESTful, Flask-JWT)
• Database integration with Flask-SQLAlchemy
FastAPI Framework:
• FastAPI fundamentals
• Asynchronous programming (async/await)
• Pydantic models for validation
• Automatic API documentation
• Dependency injection
• Background tasks and WebSockets
• Project: Build and deploy REST APIs with both Flask and FastAPI
Module 2: Machine Learning & Deep Learning Foundations
Classical Machine Learning
• Supervised and unsupervised learning
• Model evaluation and validation
• Feature engineering and selection
• scikit-learn ecosystem
• Project: End-to-end ML pipeline
Deep Learning Fundamentals
• Neural networks architecture
• Backpropagation and optimization
• CNNs and computer vision basics
• RNNs and sequence modelling
• PyTorch/TensorFlow fundamentals
• Lab: Image classifier and text classifier
Advanced Deep Learning
• Transformers architecture deep-dive
• Attention mechanisms
• Transfer learning
• Model optimization techniques
• Project: Fine-tune a pre-trained model
Module 3: Large Language Models & Foundation Models
LLM Fundamentals
• Transformer architecture in detail
• Pre-training objectives (CLM, MLM)
• Tokenization strategies (BPE, WordPiece, SentencePiece)
• Model architectures: GPT, BERT, T5, LLaMA
• Scaling laws and emergent abilities
• Lab: Explore Hugging Face Transformers
Advanced LLM Techniques
• Fine-tuning strategies (Full, PEFT, LoRA, QLoRA)
• Instruction tuning and RLHF
• Constitutional AI and preference learning
• Context window optimization
• Model quantization (4-bit, 8-bit)
• Project: Fine-tune LLaMA on domain-specific data
Prompt Engineering & In-Context Learning
• Zero-shot, one-shot, few-shot learning
• Chain-of-Thought (CoT) prompting
• Tree-of-Thoughts (ToT) reasoning
• Self-consistency and voting mechanisms
• Prompt templates and optimization
• Adversarial prompting and jailbreaking defense
• Lab: Build a prompt engineering toolkit
Module 4: Retrieval-Augmented Generation (RAG)
RAG Foundations
• Vector embeddings and semantic search
• Vector databases (Pinecone, Weaviate, ChromaDB, FAISS)
• Chunking strategies and document processing
• Hybrid search (semantic + keyword)
• Reranking techniques
• Lab: Build a basic RAG system
Advanced RAG Techniques
• Multi-query RAG
• HyDE (Hypothetical Document Embeddings)
• Parent-child chunking
• RAG fusion and ensemble methods
• Metadata filtering and routing
• GraphRAG and knowledge graphs
• Project: Production-ready RAG application with evaluation
Module 5: Agentic AI & Multi-Agent Systems
Agent Foundations
• What are AI agents? Agent architectures
• ReAct (Reasoning + Acting) pattern
• Tool use and function calling
• Agent memory systems (short-term, long-term)
• Planning and reasoning strategies
• Lab: Build a simple ReAct agent
Agent Orchestration Frameworks
• LangChain and LangGraph
• CrewAI for multi-agent collaboration
• AutoGen framework
• Agent communication protocols
• Model Context Protocol (MCP)
• Project: Multi-step task automation agent
Multi-Agent Systems
• Agent roles and specialization
• Collaboration patterns (hierarchical, collaborative, competitive)
• Agent coordination and negotiation
• Consensus mechanisms
• Multi-agent workflows
• Lab: Build a research team of agents
Advanced Agent Capabilities
• Code generation and execution agents
• Web browsing and information gathering
• Autonomous decision-making
• Agent evaluation and benchmarking
• Safety and sandboxing
• Project: Autonomous research and analysis agent
Module 6: Multimodal AI
Vision and Language Models
• CLIP, BLIP, LLaVA architectures
• Image captioning and VQA
• Text-to-image (Stable Diffusion, DALL-E)
• Image-to-text applications
• Lab: Build a multimodal chatbot
Audio and Video AI
• Speech recognition (Whisper)
• Text-to-speech synthesis
• Audio generation (MusicGen)
• Video understanding and generation
• Project: Multimodal content creation system
Module 7: LLMOps & Production Deployment
Deployment & Scaling
• Model serving (vLLM, TGI, Ollama)
• Cloud platforms (AWS Bedrock, Azure OpenAI, GCP Vertex AI)
• Inference optimization (batching, caching, quantization)
• API design for LLM services
• Load balancing and auto-scaling
• Cost optimization strategies
• Lab: Deploy LLM on cloud infrastructure
Monitoring & Evaluation
• Observability and logging (LangSmith, Weights & Biases)
• Evaluation frameworks (RAGAS, TruLens)
• A/B testing for prompts and models
• Quality metrics (BLEU, ROUGE, BERTScore, human eval)
• Hallucination detection
• Safety guardrails and content filtering
• Project: Production monitoring dashboard
Module 8: Advanced Topics & Ethics
Responsible AI & Ethics
• Bias detection and mitigation
• Fairness in AI systems
• Privacy and data governance
• AI alignment and safety
• Regulatory compliance (GDPR, AI Act)
• Red teaming and adversarial testing
• Case Study: Ethical AI implementation
Cutting-Edge Research
• Constitutional AI and preference learning
• Mixture of Experts (MoE) models
• Sparse models and efficient architectures
• Long-context models (>100K tokens)
• Reasoning models (o1, o3)
• Agent-computer interfaces
• Future trends in GenAI
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