Akswave Trainings

Advance Your Career with Oracle, Cloud & Data Science Experts

Learn from Oracle Certified Masters through real-time projects, hands-on labs, and expert mentorship. AKSWAVE Trainings helps you build practical skills, and advance your IT career.

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Trainer

NA yrs exp

Duration

90 Days

Recordings

140 Hrs

Material

Reading and LAB guide

Online LAB

Server 24x7 available

Course Overview

If you're looking for a Data Analytics with Generative AI and Agentic AI course in Chennai, you're in a great position, as Chennai is an emerging hub for AI, data science, and technology education. Many institutes in Chennai offer courses that cover the latest developments in Generative AI, autonomous intelligent systems, and data analytics. Below is a detailed overview of the curriculum and where you can explore similar courses in Chennai.

  • Understand Data Analytics and Generative AI Fundamentals: Learn core concepts of data analytics, business intelligence, Generative AI, and Agentic AI, including real-world applications across industries.
  • Develop Python and Data Analysis Skills: Use Python, NumPy, and Pandas for data cleaning, manipulation, transformation, and exploratory data analysis.
  • Perform Data Visualization and Statistical Analysis: Create meaningful visualizations using Matplotlib, Seaborn, and Plotly, and apply statistical techniques to derive business insights.
  • Work with SQL and Databases for Analytics: Write SQL queries to retrieve, filter, join, and analyze data from relational databases for business reporting and analysis.
  • Work with Large Language Models and AI APIs: Build intelligent applications using OpenAI, Claude, Gemini, Hugging Face, and open-source LLMs through API integration.
  • Master Prompt Engineering and LLM Application Development: Design effective prompts, optimize AI responses, and build chatbots and intelligent assistants using LangChain and modern AI frameworks.
  • Build Retrieval-Augmented Generation (RAG) and Vector Database Applications: Develop advanced AI systems using embeddings, semantic search, and vector databases such as Pinecone, Chroma, and Weaviate.
  • Develop Autonomous Agentic AI Systems and Deploy Real-world Applications: Build autonomous agents and multi-agent systems using LangGraph, CrewAI, and deploy production-ready AI solutions with monitoring, security, and cost optimization.
  • ✅ Real-time GenAI Projects | ✅ Hands-on Labs |✅ Certification & Career Support

Who should go for this training?
The following professionals can go for this course:

  • Data scientists, ML engineers, software engineers transitioning to GenAI  

What are the pre-requisites for this Course?

Required:

  • Strong Python programming skills (async, OOP, design patterns)
  • Solid understanding of ML/AI fundamentals
  • Experience with APIs and integrations
  • Understanding of distributed systems concepts

Recommended:

  • Experience with LangChain or similar frameworks
  • Familiarity with LLMs and prompt engineering
  • DevOps/deployment experience
  • Knowledge of databases and data structures

Course Overview

Course Content

Expand All
  • What is Data Analytics vs Data Science vs Business Intelligence
  • Roles and responsibilities: Business Analyst, Data Analyst, BI Developer
  • Analytics workflow: Data collection → Analysis → Visualization → Insights → Action
  • Real-world case studies from industry (finance, retail, e-commerce, healthcare)
  • Career paths and salary benchmarks in analytics
  • Installing Python, Jupyter Notebook, VS Code
  • Package managers: pip, conda
  • Setting up virtual environments
  • Introduction to IDE best practices

  • Language fundamentals: variables, data types, operators
  • Control flow: if-else, loops (for, while)
  • String manipulation and formatting
  • Lists, tuples, dictionaries for data handling
  • Functions: defining, parameters, return values
  • NumPy arrays vs Python lists: performance benefits
  • Array creation, indexing, slicing
  • Mathematical operations on arrays
  • Broadcasting concepts
  • Statistical functions (mean, median, std, percentile)
  • Practical: Working with multi-dimensional datasets
  • Series and DataFrame fundamentals
  • Creating DataFrames from CSV, Excel, SQL databases
  • Indexing and selecting data
  • Viewing and inspecting data (head, tail, info, describe)
  • Handling missing values (dropna, fillna, interpolation)
  • Removing duplicates
  • Data type conversions
  • Handling outliers
  • Filtering and sorting
  • Groupby operations and aggregations
  • Pivot tables and crosstabs
  • Merging, joining, concatenating DataFrames
  • DateTime handling
  • Resampling and rolling windows
  • Trend analysis
  • Understanding data distribution
  • Identifying patterns and relationships
  • Statistical thinking for analysts
  • Types of analysis: univariate, bivariate, multivariate
  • Hypothesis formation from data exploration

  • Figure and axis objects
  • Line plots, bar charts, histograms, scatter plots
  • Customization: colors, markers, line styles
  • Legend, labels, titles, annotations
  • Grid and axis formatting
  • Multiple plots: subplots and figure layouts
  • Statistical visualization concepts
  • Categorical plots: bar, box, violin, strip plots
  • Distribution plots: histograms, KDE plots
  • Relationship plots: scatter, regression plots, heatmaps
  • Color palettes and styling
  • FacetGrid for multi-dimensional analysis
  • Plotly basics: interactive charts and hover information
  • Creating dashboards with Streamlit
  • Deploying Streamlit apps to cloud (Streamlit Cloud, Heroku)
  • Real-time data updates
  • Dashboard best practices
  • Principles of data visualization
  • Choosing the right chart for the message
  • Narrative structure: context, conflict, resolution
  • Presenting insights to non-technical stakeholders
  • Creating compelling business reports
  • Central tendency: mean, median, mode
  • Spread: range, variance, standard deviation, IQR
  • Skewness and kurtosis
  • Outlier detection and handling (IQR method, Z-score)
  • Weighted statistics
  • Fundamentals: sample space, events, probability rules
  • Conditional probability and Bayes' theorem
  • Independence and dependence
  • Real-world application scenarios
  • Sampling concepts and distributions
  • Confidence intervals: for mean and proportion
  • Hypothesis testing: null vs alternative hypotheses
  • T-tests, Z-tests, chi-square tests
  • P-values and significance levels
  • Type I and Type II errors
  • SELECT, WHERE, ORDER BY, LIMIT
  • Filtering with AND, OR, NOT, IN, BETWEEN
  • DISTINCT and COUNT
  • GROUP BY and aggregation functions (SUM, AVG, MAX, MIN, COUNT)
  • HAVING clause
  • INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN
  • Self-joins and multiple joins

  • What is Generative AI vs Traditional ML
  • GenAI applications across industries (content, code, analytics, customer service)
  • The GenAI landscape: LLMs, Diffusion models, Multimodal models
  • Business use cases and ROI analysis
  • Ethical considerations and responsible A
History and Evolution
  • From word embeddings (Word2Vec) to transformers
  • GPT-3 vs GPT-4 vs Claude vs Llama evolution
  • Understanding the transformer architecture (conceptual)
How LLMs Work
  • Tokens and tokenization
  • Attention mechanisms (high-level)
  • Training process: next token prediction
  • Context windows and token limits
  • Temperature, top-k, top-p sampling explained
  • Python fundamentals for LLM applications
  • NumPy for numerical operations
  • Pandas for handling structured data
  • Statistics essentials for model evaluation
  • Setting up ML development environment (Jupyter, VS Code, Colab)
Cloud-Based LLM APIs
  • OpenAI API (GPT-4, GPT-3.5): authentication, rate limiting, cost optimization
  • Google Cloud Vertex AI: Gemini models, deployment options
  • Anthropic Claude API: working with long context windows
  • Azure OpenAI: enterprise deployment
Open Source Models
  • Hugging Face ecosystem: model hub, model cards, licensing
  • Llama 2, Mistral, CodeLlama: deployment and usage
  • Running models locally: HuggingFace Transformers, Ollama
  • Quantization: reducing model size for edge deployment
Prompt Structure
  • System prompts vs user prompts
  • Role-based prompting
  • Few-shot vs zero-shot prompting
  • Chain-of-thought prompting for complex reasoning
Advanced Techniques
  • Prompt injection and security
  • Template-based prompts for scalability
  • Evaluating prompt quality
  • Iterative prompt optimization
Core Concepts
  • LLMs, Chains, Agents, Memory
  • Prompt templates and formatting
  • Output parsing and validation
  • Sequential chains and branching logic
Working with LangChain
  • Integrating with OpenAI, HuggingFace, local models
  • Building chatbots with conversation memory
  • Document loaders and text splitters
  • Chaining multiple LLM call
Why RAG?
  • Hallucination problem in LLMs
  • Keeping models current without retraining
  • Domain-specific knowledge integration
  • Cost optimization vs accuracy tradeoff
RAG Workflow
  • Data ingestion pipeline
  • Chunking and preprocessing strategies
  • Embedding generation and semantic search
  • Retrieval ranking and reranking
  • LLM generation with context
Introduction to Vector Databases
  • Why embeddings and vector similarity matter
  • Similarity metrics: cosine, L2, dot product
  • Vector database landscape: Pinecone, Weaviate, Qdrant, Milvus, Chroma
Using Vector Databases
  • Creating and managing vector indexes
  • Embedding models: OpenAI embeddings, open-source models
  • Storing and retrieving vectors
  • Metadata filtering and hybrid search
  • Scaling vector databases
End-to-End RAG Pipeline
  • Document ingestion: PDF, web, database sources
  • Text preprocessing: cleaning, chunking, overlap strategies
  • Embedding storage and indexing
  • Query expansion and multi-step retrieval
  • Context ranking and relevance filtering

LangChain + Vector DB Integration
  • Retrievers in LangChain
  • Prompt engineering for RAG
  • Answer generation with source attribution
  • Handling multi-document retrieval
Evaluation & Optimization
  • Retrieval quality metrics: precision, recall, MRR, NDCG
  • Generation quality: semantic similarity, BLEU, ROUGE, LLM-as-judge
  • Latency and cost optimization
  • A/B testing retrieval strategies
Project Options:
1. RAG-based Q&A System: Build chatbot for company knowledge base
2. Fine-Tuned Customer Support Bot: Specialize model for domain-specific queries
3. Content Generation Platform: Multi-document synthesis with custom guidelines
4. Code Assistant: Fine-tuned model for specific programming context
Paradigm Shift: RAG to Agents
  • Limitations of static RAG systems
  • Why autonomous agents matter in 2026
  • Agent vs Chatbot vs Automation: fundamental differences
  • Business applications: autonomous research, customer support, operations, data analysis
The Agent Architecture
  • Perception (sensing inputs and context)
  • Planning (deciding what to do)
  • Action (executing tasks)
  • Reflection (learning and improvement)
  • Multi-agent collaboration patterns
Frameworks Overview
  • Crew AI: orchestration and specialization
  • Langraph: control flow and state management
  • AutoGen: multi-agent conversation framework
  • n8n: low-code agent workflows
  • Comparison and use case matrix
Agent Capabilities
  • Tool calling and function execution
  • Long-horizon reasoning and planning
  • Error recovery and retry logic
  • Learning from interactions
LLM Foundations (Advanced)
  • Transformer architecture deep dive
  • Context windows and memory constraints
  • Token budgeting for agent conversations
  • Function calling (OpenAI, Anthropic, open-source models)
  • Tool use best practices
Python for Agents
  • Async programming for concurrent agent tasks
  • Design patterns: factory, observer, state machine
  • Event-driven architecture
  • Testing and mocking agent behaviors
Agent Components
  • System prompt design for agent behavior
  • State management: memory, knowledge, goals
  • Tool definition and schema specification
  • Response parsing and validation
Tool Ecosystem
  • Creating custom tools for agent use
  • Tool documentation standards
  • Grounding agents in reality (APIs, databases, web search)
  • Tool chaining and composition
  • Safety constraints and permission systems
Core Concepts
  • Nodes and edges: defining agent behavior graph
  • State management: persistent and ephemeral
  • Branching and conditional flows
  • Recursion and looping in graphs
Building Agents with LangGraph
  • Define tool schemas and LLM function calling
  • Implement agent loops with LangGraph
  • Handle hallucinations and invalid tool calls
  • Streaming and progressive output
  • Debugging agent behavior
Advanced Patterns
  • Sub-agents and hierarchical control
  • Interrupt and human-in-the-loop workflows
  • Cross-cutting concerns: logging, monitoring, tracing
Crew AI Architecture
  • Crew, Agents, Tasks, Tools concepts
  • Agent roles and specialization
  • Task dependencies and workflows
  • Context passing between agents
Building Crews
  • Defining specialized agents (analyst, executor, validator)
  • Creating sequential and parallel task flows
  • Agent memory: short-term and long-term
  • Crew callbacks for monitoring
  • Output formatting and validation
Multi-Agent Patterns
  • Manager pattern: centralized decision-making
  • Consensus pattern: voting and agreement
  • Debate pattern: adversarial collaboration
  • Pipeline pattern: sequential processing
API Integration
  • REST APIs: rate limiting, authentication, error handling
  • Database connections: SQL queries, data updates
  • Web scraping: dynamic and static content
  • Third-party integrations: Slack, Gmail, Salesforce
External Data Sources
  • Real-time data feeds and market data
  • Internal data lakes and data warehouses
  • Streaming data: WebSockets, Server-Sent Events
  • Data freshness and cache invalidation

Conversation Patterns
  • Sequential conversations: strict ordering
  • Parallel conversations: concurrent agent execution
  • Star topology: central coordinator
  • Mesh topology: direct agent-to-agent communication
  • Dynamic topologies: agents join/leave at runtime
Planning Strategies
  • Reactive planning: immediate response
  • Deliberative planning: forward search
  • Hierarchical planning: decomposing complex tasks
  • Planning with constraints and resources
n8n Introduction
  • Workflow automation fundamentals
  • Nodes and connections
  • Triggers and conditions
  • Data transformation nodes
Building Agent Workflows in n8n
  • Calling external LLM agents from n8n
  • Parallel and conditional execution
  • Error handling and retry strategies
  • Webhook integration for external events
  • Scheduling and recurring workflows
Scaling Workflows
  • Resource management and concurrency limits
  • Database storage of workflow state
  • Audit logging and workflow history
  • Workflow versioning and rollback
Chain-of-Thought and Tree-of-Thought
  • Structured reasoning: step-by-step decomposition
  • Branching reasoning: exploring multiple paths
  • Combining human reasoning with LLM capabilities
Retrieval-Augmented Agents (RAG + Agents)
  • Information retrieval in agent loops
  • Dynamic knowledge updates
  • Context management in long conversations
  • Knowledge graph reasoning with agents
Task Planning
  • Goal decomposition and sub-goals
  • Dependency graphs and critical path analysis
  • Resource constraints and scheduling
  • Plan validation and contingency handling
Search Strategies
  • Breadth-first and depth-first search
  • Best-first search and heuristics
  • Monte Carlo Tree Search for complex decisions
  • A* and informed search
Failure Modes
  • Tool execution failures
  • Hallucinations and incorrect reasoning
  • Resource exhaustion and timeouts
  • Invalid state and contradiction detection
Recovery Strategies
  • Retry logic with exponential backoff
  • Alternative tool invocation
  • User escalation and human-in-the-loop
  • Graceful degradation and fallback modes
Task Success Metrics
  • Goal achievement rate
  • Task completion time
  • Resource efficiency (API calls, tokens, cost)

Quality Metrics
  • Plan quality and optimality
  • Reasoning coherence and correctness
  • Safety and constraint satisfaction
  • User satisfaction and feedback
System Design
Logging and Tracing
  • Structured logging for agent interactions
  • Distributed tracing across agent calls
  • LLM cost tracking and optimization
  • Performance metrics collection
Monitoring Dashboards
  • Agent health and availability
  • Task success rates and latencies
  • Error rates and recovery performance
  • Cost analysis and budget controls
Token and API Optimization
  • Caching strategies for repeated queries
  • Prompt compression techniques
  • Model selection: cost vs performance tradeoff
  • Batch processing for efficiency
Infrastructure Costs
  • Compute resource right-sizing
  • Spot instances and preemption handling
  • Database indexing for query efficiency
Agent Security
  • Tool access controls and sandboxing
  • Input validation and prompt injection prevention
  • Output filtering and PII detection
  • Audit trails and compliance logging

Governance
  • Agent behavior policies and constraints
  • Model governance and versioning
  • Data privacy and retention policies
  • Regulatory compliance (GDPR, SOX, etc.)
Project Scope:
Build an autonomous system that solves a complex business problem involving multiple specialized agents

Project Options:
1. Autonomous Research Agent: Multi-source research synthesis, fact-checking, and report generation
2. Customer Service Crew: Ticket routing, problem-solving, escalation, and feedback integration
3. Data Analysis Team: Data exploration, statistical analysis, visualization, and insight generation
4. Sales Operations Agent: Lead qualification, data enrichment, meeting scheduling, follow-up
5. DevOps Automation Crew: Infrastructure monitoring, incident response, and auto-remediation

Requirements:
  • Minimum 2 specialized agents with distinct roles
  • Integration with 3+ external tools/APIs
  • Sophisticated reasoning and error handling
  • Production deployment with monitoring
  • Cost analysis and optimization report

Deliverables:
  • System architecture diagram (agents, tools, data flows)
  • Complete source code (GitHub with documentation)
  • Deployed system (API accessible)
  • Performance benchmarks and cost analysis
  • User guide and maintenance documentation
  • Presentation with business impact metrics
Core Frameworks: Crew AI, LangGraph, AutoGen
  • Automation Platform: n8n
  • LLM APIs: OpenAI, Google Vertex AI, Anthropic Claude
  • Databases: PostgreSQL, MongoDB, Pinecone (vector DB)
  • Message Queues: RabbitMQ, Kafka
  • Containerization: Docker, Kubernetes
  • Cloud Platforms: AWS, GCP, Azure
  • Monitoring: Prometheus, Grafana, DataDog, Langfuse
  • Languages: Python 3.10+
Weekly coding assignments and design challenges
  • 3 mini-projects (single agent, multi-agent crew, n8n workflow)
  • Capstone project with complete deployment
  • Portfolio-ready production system
  • Advanced certification in Agentic AI

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Modes of Training

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Classroom Training

Lives interactive sessions delivered in our classroom by our expert trainers with real-time scenarios.

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Online Training

Learn from anywhere over internet, joining the live sessions delivered by our expert trainers.

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Self-Pace Training

Learn through pre-recorded video sessions delivered by experts with your own pace and timings

For Coporate Training, We provide customized content and delivered by industry experts with complete practical demonstration, discussions and exercises based on practical use cases.

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Our Key Highlights

Unique Benefits included in this training

  • BEST TRAINER : Having NA yrs exp and delivered more than 60 batches
  • QUALITY CONTENT : Covers in-depth and industry-relevant advance topics.
  • LAB ACCESS : Practice hands-on with dedicated online servers, anytime, anywhere.
  • BEST PRICE : Best value training at a competitive price.
  • SESSION RECORDINGS : Access to session recordings for easy revision.
  • REAL-TIME SCENARIOS : Learn through practical scenarios and live projects.
Key Benefits

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CLASSROOM TRAINING

This Course Includes:
  • Delivered by our experts having NA yrs exp
  • 90 Live classroom sessions
  • Access for Recorded videos
  • Reading material and Lab activity guide
  • One-to-one dedicated server access for practice
  • 200+ Hrs of Lab practices
  • 100% practical-oriented classes
  • Real-time projects and certification guidance
  • Get certificate on course completion
  • Job assistance
  • Certified technical assistance
06-Mar

10:00am to 11:30am IST

90 days (Mon-Fri)

11-Mar

10:00am to 11:30am IST

90 days (Mon-Fri)

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Course Fee : ₹ 45,000/-

Discount : ₹ 10,000/- (22%)

Offer Price : ₹35,000/-

ONLINE TRAINING

This Course Includes:
  • Delivered by our experts having NA yrs exp
  • 90 Virtual online sessions
  • Access for  140 Hrs  of Recorded videos
  • Reading material and Lab activity guide
  • 24x7 dedicated online AksWave server access
  • 200+ Hrs of Lab practices
  • 100% practical-oriented classes
  • Real-time projects and certification guidance
  • Get certificate on course completion
  • Job assistance
  • Technical support thru chat and email
06-Mar

10:00am to 11:30am IST

90 days (Mon-Fri)

12-Mar

10:00am to 11:30am IST

90 days (Mon-Fri)

Not yet scheduled

Not yet scheduled

Not yet scheduled

Course Fee : ₹ 55,000/-

Discount : ₹ 17,000/- (31%)

Offer Price : ₹38,000/- $366

SELF-PACED LEARNING

This Course Includes:
  • Access for  140 Hrs  of Recorded videos
  • Reading material and Lab activity guide
  • 24x7 dedicated online AksWave server access
  • 200+ Hrs of Lab practices
  • 100% practical-oriented classes
  • Real-time projects and certification guidance

₹32,000/-

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