Certified Agentic AI Training | Job-Oriented Course | Bangalore

Certified Agentic AI Course for Career Transition

Course Outcome:

Hands-On Coding Experience in Python Development
Building Gen AI Products at Scale
Experience in Building Solutions using Langchains, LangGraphs, Crew AI, RAG & Multi- Agents
Experience in Automated CI/CD Pipelines using MLOps/LLMOps
Experience in deploying solutions on a cloud platforms
Ability to explain complex technical concepts to various audiences
Experience in Analyzing large and complex datasets

Cohort Start Date

2nd April, 2026

Live Instructor-Led Classes

Slot:1 - Weekdays: 8PM - 9:30 PM
Slot:2 - Weekends(Sat-Sun): 7AM -10AM

Program Duration

6 Months

Certified Agentic AI Training Syllabus

Learn & Work on End-to-End Agentic AI Projects on Cloud

By End of This Training, You Will Gain:

Hands-On Coding Experience in Python Development
Building Gen AI Products at Scale
Experience in Building Solutions using Langchains, LangGraphs, Crew AI, RAG & Multi- Agents
Experience in Automated CI/CD Pipelines using MLOps/LLMOps
Experience in deploying solutions on a cloud platforms
Ability to explain complex technical concepts to various audiences
Experience in Analyzing large and complex datasets

BEPEC Help you, Setup below Lab:

Windows Laptop or MacBook(Pre-requisite)
Python above 3.11 Version
Vs Code, Jupyter & Cursor
MySQL or Postgres
Github Account
AWS/Azure/GCP Cloud Account
API Keys from OpenAI, Gemini & Grok
LLM Models from Hugging Face

Mastering Python[Zero to Hero]

Introduction to Python

01:02:54
Why Python, Value, Variable, Function, Library[Roadmap on Python]

01:04:10
IDE in Python, Different Data Types

01:01:53
List, Tuple, Set & Dictionary Overview

57:16
Different List Methods

55:48
Different Tuple Methods

01:04:52
Set & Frozenset

01:00:50
Dictionary & String Manipulations

50:56
Overview on Loops, If Statements, UDFs, Escape Sequences, Lambda

01:03:15
Types of Operators, Conditional Statements

23:42
While Loop, List Comprehension, Break, Continue, Arguments

01:01:18
Functions, Escape Sequences, Lambda Functions

52:23
Hackathon-1

54:32
Introduction to OOPS

16:16
Instance Variable, Class Variable, Class Method

56:53
Association vs Composition & Aggregation

50:02
Oops Concept

36:29
Encapsulation, Inheritance

01:07:07
Polymorphism, Method OverLoading, Method Overriding

53:59
Introduction to Pandas

01:05:22
Data Analysis using Pandas

01:34:29
Introduction to Numpy

57:26
Different Numpy Commands

01:03:00
Introduction to Data Visualisation

51:41
Data Visualisation using Matplotlib

01:00:20
Data Visualization using Seaborn

59:34
Data Visualization using Plotly

41:44
Why Data Cleaning?

47:05
Data Cleaning with Sklearn & Pandas

49:57
Regular Expression Basics

54:01

Advanced Data Structures and Algorithms

Introduction to Power Bi
Non-Primitive Data Structures
Non-Linear Data Structures
What is an Algorithm
Theory & Code Implementation of Linked List
Stacks & Queues Assignment
Coding Stack Data Structure
Coding Queue Data Structure
Tree Data Structures
Types of Tree Data Structure
Tree Traversal
BFS Traversal
Bubble Sort Theory
Bubble Sort Code Implementation
Selection Sort Theory
Selection Sort Code Implementation
Insertion Sort Theory
Insertion Sort Code Implementation
Merge Sort Theory
Merge Sort Code Implementation
Quick, Merge Sort Performance
Quick Sort Theory
Linear Search & Bisection Search

Applied Statistics

What is Data? Various Types of Data
Levels of Measurement
Categorical Variables & Visualisation Techniques
Continuous Variables and Visualisations
Measures of Central Tendency
Measuring Skewness
Measures of Dispersion
Covariance & Correlation Coefficient
Box & Whisker Plot
Outlier Identification
Treating Outliers
Sampling Techniques
Statistical Analysis Consideration
Types of Analytics
Sample vs Population
Introduction to Inferential Statistics
Law of Large Numbers
Central Limit Theorem
Probability Distributions
Conditional Probability
Continuous Probability Distribution & Discrete Probability Distribution
PMF, CDF, PDF, PPF in Probability
Binomial Distribution
Poisson Distribution
Normal Distribution
Exponential Distribution
Laplace Distribution
Log-Normal Distribution
Standard Error
Confidence Interval Calculations
Z-Table for Confidence Interval
T-Table for Confidence Interval
Hypothesis Testing
Null & Alternate Hypothesis
Type-1 Error & Type-2 Error
2-Sample T Test
ANOVA Test
p-Value Calculations
Chi-Square Test
Project Demonstration using all the Statistical Techniques

Statistics with Real-Time Project Demonstration on EDA

Real-Time Project Demonstration on Probability Distribution, Hypothesis Testing

Probability Distribution, Random Variable, Binomial, Poisson, Exponential
Normal Distribution
Real-Time Project Demonstration on Probability
Hypothesis Testing Part-1
Hypothesis Testing Part-2
How to Calculate P-Value? Coding Hypothesis Testing
How to solve PDF, CDF, PMF?
Chebyshevs, Log, Power Law, Q-Q, CLT

Unsupervised Learning with Real-Time Projects

Supervised vs Unsupervised Learning

21:51
Fundamentals of Machine Learning Part-1

32:19
Fundamentals of Machine Learning Part-2

48:41
Insights from Train & Test Accuracy

21:23
Associate Rules Part-1

23:02
Associate Rules Part-2

02:27
Problem Identification & Approach Designing

01:02:13
Associate Rules Scripting

57:15
Why to do Dimensionality Reduction? Project Implementation using PCA & T-SNE

01:03:36

Feature engineering Techniques

Feature Scaling Techniques
Normalisation
Standardisation
Min Max Scaler
Robust Scaler
Label Encoding Technique
One-Hot Encoding Technique
Dummies Technique
Ordinal Encoding Technique
Imputation Technique
Feature Selection Techniques
Filter Method: Feature Importance
Filter Method: Fishers Score Technique
Filter Method: Chi-Square Technique
Filter Method: Variance Threshold
Filter Method: Correlation Coefficient
Filter Method: Mutual Information
Wrapper Method: Forward Selection Technique
Wrapper Method: Backward Selection Technique
Wrapper Method: Recursive Feature Elimination Technique
Wrapper Method: Exhaustive Feature Selection Technique
Wrapper Method: Sequential Feature Selection Technique
Embedded Methods: Regularisation
Embedded Method: Elastic Net Regression
Embedded Method: Decision Tree
Outlier Treatment
Over Sampling Technique
Under Sampling Techniques

Supervised Learning Algorithms

How to Prepare Machine Learning Math for Interviews

06:52
Why to use Linear Regression?

06:13
What is Linear Regression

04:42
Case Study on Linear Regression

06:21
Math Behind Linear Regression Part-1

07:03
Math Behind Linear Regression Part-2

06:25
Math behind OLS Technique

04:32
Assumptions of Linear Regression

23:08
Evaluation Metrics for Regression Model

15:50
Accuracy Improving Techniques

14:29
Coding Linear Regression Model
Regularisation Techniques

08:56
Why Logistic Regression

04:27
Math Behind Logistic Regression

17:44
Evaluation Metrics Behind Classification Algorithms

17:10
ROC & AUC Curve

10:20
Coding Logistic Regression Model
Introduction to Decision Tree

06:12
Intuition Behind Decision Tree

07:32
Math Behind Decision Tree

16:03
Math Behind Decision Tree using GINI

04:30
Drawbacks of Decision Tree

05:07
Random Forest and Gradient Boosting

13:08
Coding Decision Tree, Random Forest, GB
SMOTE Technique to Handle Imbalanced Dataset

06:35
Filter Method, Wrapper Method, Embedded Method – Feature Selection Techniques

08:47
Coding Feature Selection Techniques
Math Behind KNN

32:21
Math Behind SVM

47:20
Plotting SVM
Introduction to PCA
Math behind PCA
Coding PCA
AutoML Using Pycaret

Probability Distributions, Hypothesis Testing

Probability Distribution, Random Variable, Binomial, Poisson, Exponential

13:07
Normal Distribution

23:00
Real-Time Project Demonstration on Probability

01:34:20
Hypothesis Testing Part-1

13:07
Hypothesis Testing Part-2

05:58
How to Calculate P-Value? Coding Hypothesis Testing

01:15:23
How to solve PDF, CDF, PMF?

10:03
Chebyshevs, Log, Power Law, Q-Q, CLT

22:14

Mastering NLP, NLU, NLG

MLOps

End-to-End Deployment Using MLOps & Streamlit

03:11:38
Setup Virtual Environment

04:49
Docker Installation

03:47
How to Activate Virtual Environment

03:03
Docker Desktop

02:51
Regression Model using Pycaret

13:26
Regression Model using Pycaret Part-2

02:10
Interpretability of Model using SHAP

04:46
What is SHAP

01:16
Application Development using Gradio

03:31
Creating API using FASTAPI

03:54
Creating Docker Image

05:29
Model Versioning using Pycaret & MLFlow

06:51
End-to-End ML Proof of Concept

00:00

Azure Machine Learning Engineering

Introduction to Azure Environment
Training & Tuning ML Models using Azure
Working with Data in Azure
Training ML Models in Azure ML
Tuning ML Models in Azure
Azure Automated ML
Deploying & Explaining ML Models
Deploying ML Model for Real-Time Inferencing
Deploying ML Models for Batch Scoring
Responsible AI

AWS Sagemaker Studio

Introduction to Deep Learning, Tensorflow & Feed Forward Neural Networks

Building Blocks of Artificial Neural Networks
What are Optimisers and Types of Optimisers
Math Behind Feed Forward Neural Network
Math Behind BackPropagation
Why Activation Functions & Importance
Introduction to Tensorflow
FFNN-Regression Script using Tensorflow
FFNN – Classification Script Using Tensorflow

Convolution Neural Network – Math & coding

Introduction to CNN & Why CNN Algorithm
What is Object Detection & Object Classification
Intuitive Understanding Behind CNN
Math Behind CNN
CNN Coding using Tensorflow
CNN Coding using Keras
Types of CNN Networks
What is Transfer Learning
Tensorflow Hub & Various Pre-Trained Models
Using Pre-Trained Models for Object Classification
Deploying Image Classification Model in AWS
Project Demonstration: Image Classification Project

Computer Vision Mastering Using Tensorflow

Introduction to Object Detection vs Object Classification
Introduction to CNN
Convolution Matrix
Max Pooling Layer
Point-Wise & Element-Wise Convolution
Batch Normalisation
Strides & Padding
Convolution Filters
End-to-End Computer Vision

Transfer Learning OpenCV & YOLO

RNN & LSTM Coding & Math

What is RNN & Why RNN?
Limitations of RNN & Why LSTM
Intuitive Understanding Behind RNN & LSTM
Math Behind RNN
Math Behind LSTMS
Math Behind GRUs
Coding RNN, LSTM, GRU using Tensorflow
Project Demonstration: Forecasting Project using LSTMS

Mastering NLP

Introduction to NLP & NLP Applications
NLP Challenges
NLP Pre-Processing Techniques
NLP Feature Extraction
Project Demonstration: NLP Classification Project
What is N-Gram & Why N-Gram Technique
Coding N-Gram Technique
What is Word2Vec?
Word2Vec Algorithms like CBOW & Skip-Gram
Case Study: Coding Word2Vec Model
What is latent dirichlet allocation? Why LDA
What is Topic Modelling?
Case Study: Coding Topic Modelling

Mastering NLU

What is NLU & Why NLU
What is POS Tagging
Case Study: Coding POS Tagging
What is Dependency Parsing?
What is Constituency Parsing?
Case Study: Coding Dependency & Constituency Parsing
What is NER? & Usage of NER
Case Study: Coding NER
What is Coreference Resolution?
History of NLU Models
Project Demonstration: NLU Classification Model using LSTM
What is Encoder & Decoder Model
Math Behind Encoder-Decoder Models
Case Study: Coding Encoder-Decoder Model
What is Attention Based Models
Math behind Attention Based Models
Case Study: Coding Attention Based Models

Foundations of Generative AI

Large Language Models (LLMs)

Small Language Models (SLMs)

Prompt Engineering – LLMs: Lang Chains

What is Prompt Engineering
Types of Prompt Engineering
Zero-shot, One-shot, Few-shot Prompting
Role of Temperature, Top-k, Top-p, Repetition Penalty
Chain of Thought (CoT), Tree of Thought, AutoPrompt, ReAct
Introduction to Lang Chains
Using LLMs using Lang Chains
Best Way to Improve LLMs Accuracy
Doing Prompt Engineering using Lang Chains
Prompt Injection, Jailbreaking, and Mitigation Strategies
Instruction-based Prompts (e.g. Summarise, Rewrite)
Role-based Prompts
Multi-turn / Conversational Prompts
System vs User Prompts
Scratchpad & Step-by-step Reasoning
Agentic Prompts for Autonomous LLMs

LangChain Framework

Retrieval-Augmented Generation (RAG)

Amazon Bedrock & GenAI on Cloud

Agentic AI

LangGraph – Multi-Agent DAG Framework

Multi-Agent Systems

Model Context Protocol(MCP)

LLMOps & Production Deployment

LangFlow – Visual Low-Code Interface

n8n for Agent Workflow Automation

Generative AI: Transformer Models BERT, T5, ELMO

Introduction to Transformers
Scaled Dot Product Attention
Multi-Headed Attention
Introduction to Transfer Learning
Introduction to PyTorch
Fine-Tuning Transformers using PyTorch
Introduction to BERT
Wordpiece Tokenisation
Embeddings of BERT
Masked Language Modelling Task
Project Demonstration: Sentence Prediction Task
Types of BERT
BERT for Sequence Classification
BERT for Question & Answering

Generative AI: Natural Language Generation Using GPT

Generative AI: Mastering T5

Generative AI: Vision Transformers

Generative AI: Autoencoders

Generative AI: Image Based Models

What are Generative Models
Math Behind Generative Models
Project Demonstration: GANs
Types of GANs
What are Restricted Boltzmann Machines?
Math Behind Restricted Boltzmann Machines
Project Demonstration: Coding RBMs with Case Study
What is Neural Style Transfer?
Math Behind Neural Style Transfer
Project Demonstration: Coding of NST

Generative AI: Diffusion Models, Energy Based Models & NFMs

Introduction to Diffusion Models
Deep Dive into CLIP Research Paper
Deep Dive into GLIDE Research Paper
Understanding Diffusion Model Theory & Architecture
Project Demonstration on Diffusion Models
Understanding Energy Based Models Theory & Architecture
Project Demonstration on Energy Based Models
Understanding Normalising Flow Models
Project Demonstration on Normalising Flow Models

Generative AI – LLMS: Hugging Face

Deep Reinforcement Learning

What is Reinforcement Learning
RL versus ML Framework
Why RL?
Examples of RL
Limitations of RL
Terminologies of RL
Q-Learning & Q-Table Theory
Implementation of Q-Learning
Reinforcement Learning Based Q-Learning
Introduction to Rules of Frozen Lake
Implementation of Frozen Lake
Hyper Parameter Tuning
Introduction to SARSA(State-Action-Reward-State-Action)
Implementation of SARSA
Introduction to Deep Reinforcement Learning DQN
Implementation of DQN
Project Demonstration: DQN Project

Interview Questions: Frequently Asked Statistics

Introduction to Statistics
Properties of Data
Sample vs Population
Types of Sampling Techniques
Exploratory Data Analysis
Analysing Data with Applied EDA
Measures of Dispersion
Skewness & Normality
Correlation & Scatterplot
Barplot & Lineplot

MindMap for Interview Revision

Data Science Interview Revision Mindmap
Statistics Mindmap
Python Learning Mindmap
SQL Interview Mindmap
Machine Learning Accuracy Improving Techniques Mindmap
Machine Learning Math Mindmap
Real-Time Project Procedure Mindmap
ML Framework Mindmap
Power BI Interview Mindmap
NLP Interview Mindmap
Deep Learning Interview Mindmap

Interview Preparation

Course Files

Handwritten Notes

Agentic AI Certification with POCs

Our Use-case driven Agentic AI course designed to help you stand out in today’s competitive job market. Whether you’re entering the field or upskilling, this program offers a hands-on learning experience that mirrors the demands of leading companies.

From mastering Langchains, Langgraphs, Agentic AI, MCP, LLMOps, RAG, Python, Statistics, Amazon Bedrock, Langchains, OpenAI, Generative AI, Prompt Engineering, Deep Learning and Machine Learning to building predictive models and interpreting real-world datasets, you’ll gain in-demand skills that translate directly to the workplace. Our expert-led training ensures you’re equipped with practical tools, not just theory.

You’ll also complete use cases based on actual industry case studies—giving you the confidence to tackle business problems across domains such as finance, healthcare, and e-commerce.

To ensure you’re ready, the course concludes with a comprehensive assessment inspired by real interview and certification standards.

This certification is valued by over 30+ global employers, including top firms like The MathCo, Walmart, Honeywell, Bank of America, Tiger Analytics, Merck, Allianz, ToTheNew, Yes Bank, Kotak, HDFC, Suzlon —helping you showcase your capabilities and accelerate your career.

Work on Gen AI Real-Time Projects to Get Experience Certificate

Participate in BEPEC's real-time projects to gain hands-on experience that employers value. These projects not only deepen your understanding but also strengthen your resume, increasing your chances of getting shortlisted. By showcasing your ability to apply knowledge to practical scenarios, you'll stand out in interviews.

Trainer Profile

Rajeev Kanth: AI Solutions Architect | International Corporate Trainer | Founder of BEPEC Solutions
With 12 + years of hands-on industry and training experience, Rajeev Kanth is a renowned AI Architect and Educator who bridges the gap between enterprise AI adoption and real-world implementation. He has trained over 25,000 professionals worldwide and designed end-to-end AI and Data Solutions architectures for Fortune 500 clients and high-growth start-ups.

Building scalable, secure, and composable AI systems with modern stack integration.
LangChain | LangGraph | CrewAI | AutoGen | OpenAI | Anthropic | Gemini | Amazon Bedrock | Groq | n8n | LangFlow.
Vector Databases (FAISS, Chroma, Pinecone, Weaviate) | Retrieval Strategies | Prompt Optimization | LLM Evaluation.
PySpark | Azure Data Factory | Databricks | AWS Glue | Snowflake | Kafka | Airflow | Synapse Analytics.
AWS SageMaker | Azure ML Studio | Vertex AI | Ray | MLflow | Docker | Kubernetes | CI/CD.
Data Collection → Feature Store → Model Training → Deployment → Monitoring → Governance.
Responsible AI | Bias Mitigation | Data Privacy | Compliance (ISO, GDPR, SOC2).
Delivered 150 + international corporate bootcamps across India, UAE, UK and the US.
Designed and executed over 60 live AI solutions integrated with enterprise ecosystems.
Mentored CXOs and Tech Leads on AI adoption strategies and responsible AI governance.
Featured in leading AI conferences for talks on “Agentic AI and Future of Autonomous Systems.

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