Ultimate Artificial Intelligence Course Bangalore (2026)

Ultimate Artificial Intelligence Course Bangalore (2026) | Agentic AI with Internship

BEPEC’s Ultimate Artificial Intelligence course Bangalore helps you learn Artificial Intelligence from beginner level to career transition into the field of AI by providing training on essential skills like Python, LangChain, LangSmith, LangGraph, CrewAI, AutoGen, Machine Learning, Deep Learning, NLP, NLG, Databricks, AWS, Azure, Jenkins, MLOps, MCP, SLMs, Agentic AI, LLMOps, Docker, Kubernetes, Statistics, and Generative AI; all of which are essential for the current job market in 2026.

Designed for Beginners with Zero Knowledge of Python & AI
Top Curriculum for India, USA, UK & UAE Job Market
Duration: 6 Months (Weekend & Weekday Batches Available)
Flexible Time Slots Available for USA, UK, India, & UAE Students
Build Github Portfolio & LinkedIn Portfolio
Mock Interviews, Projects for Resume & LinkedIn Optimization
Live Instructor-Led Guided Hands-On Lectures
Life-Time Recording Access

Next Live-Instructor Led Classes

18th May, 2026

Batch Timing

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

Program Duration

6 Months

About Artificial Intelligence Course Bangalore

Looking for the best Artificial Intelligence Course Bangalore that actually gets you placed? You’ve landed in the right place. BEPEC Solutions is Bangalore’s most trusted Artificial Intelligence training institute, headquartered in Koramangala, with 10+ years of experience, 30,000+ placed alumni, and 500+ active hiring partners across India and globally.

Our Artificial Intelligence Course Bangalore is engineered for working professionals, freshers, and career-switchers who want to break into high-paying AI, Machine Learning, and Generative AI roles. Whether you’re searching for an AI course Bangalore with placement, an Artificial Intelligence certification in Bangalore, or the top AI training institute in Bangalore, BEPEC delivers an industry-aligned, hands-on, project-driven curriculum that today’s hiring managers actually demand.

Our AI course in Bangalore covers the complete modern AI stack:

Foundations: Python, Statistics, SQL, Mathematics for AI
Machine Learning: Supervised, Unsupervised, Ensemble Models, Model Tuning
Deep Learning: ANN, CNN, RNN, LSTM, Transformers, Attention Mechanisms
Natural Language Processing (NLP): Text processing, embeddings, transformer models
Computer Vision: Image classification, object detection, OpenCV
Generative AI: LLMs, Prompt Engineering, Fine-tuning, LangChain, LangGraph
Advanced GenAI: RAG, Self-RAG, CRAG, Agentic RAG, MoE architectures
MLOps & LLMOps: Docker, Jenkins, model deployment, monitoring
Cloud AI: AWS, Azure, Databricks, Vector Databases
Capstone Projects: End-to-end real-world AI projects for your portfolio
Real-Time Projects: To Show in Github & LinkedIn

This makes BEPEC’s Artificial Intelligence course in Bangalore with placement one of the most comprehensive and future-proof AI programs in India.

We Help You Make Career Switch!

60 - 100% Salary Hike

With BEPEC Portfolio & POCs, Learners can achieve a 60-100% Salary Hike on Average.

500+ Hiring Partners

We refer your profile to 500+ Hiring Partners across India, UAE, UK & USA

30000+ Career Transitions

From 2016 to the present, we made 30K+ Career Transitions

Instructors Real-World AI Projects Experience

About Instructor Rajeev Kanth – AI Architect and Consultant

Autonomous Enterprise Agents: Designed multi-agent systems using LangGraph for automated supply chain reconciliation and real-time inventory optimization.
Scalable RAG Architectures: Implemented high-throughput Retrieval-Augmented Generation for Pharmaceutical Products with advanced chunking and evaluation frameworks (RAGAS, TruLens).
Agentic DevOps & SRE: Built self-healing infrastructure agents that monitor, triage, and remediate cloud infrastructure optimization & security incidents autonomously
Multi-Modal Generative Workflows: Developing systems capable of generating synthetic customer data, high-fidelity video, and audio for hyper-personalized marketing at scale.
MLOps 2.0: CI/CD for LLMs, LLMOps, Model Monitoring, and Versioning with MLflow and Ray.
Responsible AI: Implementing bias detection, hallucination mitigation, and compliance guardrails (GDPR, ISO 42001, SOC2).
Data Engineering for AI: Building feature stores and real-time streaming pipelines to feed autonomous decision engines.
150+ Corporate Bootcamps: Trusted by like Walmart, Suzlon, DU Dubai, XFab Malaysia, Barclays, Bank of America, Merck, Dell, Yes Bank, Kotak, Nokia, EY, Cognizant, and BMW.
Founder of BEPEC: Pioneered the “Experience Building” model, helping thousands of learners transition into AI roles through 60+ live AI solutions and real-time POCs.

Why is BEPEC the Best Artificial Intelligence Training Institute in Bangalore?

While others teach theory, BEPEC pioneers the “Experience Building” model. We don’t just teach you coding; we make you build highly scalable, ROI-driven AI products that companies are actively hiring for.

1. The Only Course Covering Agentic AI & Gen AI We are the only institute in Bangalore offering a modern 2026 tech stack. You will master LangGraph, CrewAI, AutoGen, SLMs, Prompt Engineering, RAG (Retrieval-Augmented Generation), LLMOps, MCP, Databricks, AWS, Azure, Jenkins, Docker, K8s, and MLOps.

2. Real-World AI Projects with Internship Experience Stop showcasing simple practice projects in your resume. Instead, build live Proof of Concepts (POCs) that you can showcase on Github, LinkedIn, and in interviews.

Our Artificial Intelligence course Bangalore covers the complete AI lifecycle, starting from the absolute basics of programming to deploying scalable Gen AI agents on the cloud.

✅ 10+ years of training excellence

✅ 500+ hiring partners for direct placement

✅ Trained by a AI Architect & Consultant

✅ Live projects, real datasets, production-grade code

✅ Coverage of Generative AI, Agentic AI, RAG, SLMs, MCP, MLOps, LLMOps

✅ 137K+ YouTube subscribers and 170K+ Instagram followers.

BEPEC AI Course Live Projects You Will Build & Deploy

KOL Intelligence & HCP Engagement Engine

Graph-based KOL discovery across publications, trials, and congress talks — with an LLM layer that summarizes physician influence, therapeutic focus, and optimal engagement channel.

Stack: Neo4j · BioBERT · PubMed API · OpenSearch · Claude Sonnet · Veeva CRM · Snowflake

Autonomous Supply Chain Control Tower

Multi-agent system that monitors 12K SKUs across 80 markets, auto-resolves disruptions (port delays, supplier defaults) with planner-executor agents, and only escalates exceptions.

Stack: LangGraph · CrewAI · SAP MCP · Kafka · Neo4j · OR-Tools · Databricks · Power BI

Enterprise MLOps Platform for 180+ Models

Self-service MLOps platform with centralized feature store, model governance, drift monitoring, and auto-retraining — manages 180+ models across 80 markets with 100% SLA.

Stack: MLflow · Feast · Evidently AI · Airflow · Kubernetes · Databricks · Unity Catalog · Grafana

Always-On MMM Platform with GenAI Insights

Bayesian MMM across 40+ markets measuring TV, digital, retail media, and trade spend — with a GenAI co-pilot that explains attribution shifts in plain English to brand managers.

Stack: PyMC · Meridian · Snowflake · dbt · Streamlit · Claude Sonnet · Databricks · LangChain

Adaptive Fraud Detection with Agentic Features

Fraud platform using agentic AI for automated feature engineering — graph neural networks surface mule rings, adaptive thresholds cut false positives by 47% without raising escape rate.

Stack: PyTorch Geometric · Featuretools · Flink · Feast · Redis · Kafka · Snowflake · Neo4j

HR & Recruitment: The "Bi-Directional" Resume Matcher

A system that goes beyond keyword matching to simulate an “Initial Technical Screen” through an agentic conversation with the candidate’s data.

Intelligent Sales Agent for B2B Growth

Agent that prioritizes accounts, drafts personalized outreach from 10-Ks and product signals, books meetings, and preps the AE with a pre-call brief — lifting pipeline by 2.3x.

Stack: LangGraph · Claude Opus · Salesforce MCP · Gong API · Exa · Clay · Pinecone · Temporal

Risk-Based Commercial Auto Pricing Engine

Predictive pricing engine fusing telematics, FMCSA safety, and claims history to produce underwriter-grade quotes in under 200ms with reason codes for every price point.

Stack: XGBoost · GLM · MLflow · Feast · FastAPI · Snowflake · Tableau · Azure Databricks

Cognitive Part-Match Engine for Obsolete SKUs

Multimodal matcher that finds replacements for obsolete industrial parts using specs, CAD drawings, and legacy PDFs — response time drops from days to seconds at ~100% match accuracy.

Stack: CLIP · Claude Sonnet Vision · AWS Textract · Weaviate · LangChain · FastAPI · PostgreSQL

Generative AI
Agentic AI
Deep Learning Algorithms
Computer Vision
Python
DSA
SQL
Snowflake
Prompt Engineering
Hybrid RAG
Multi-Agents
NLUs
NLP
Transformers
Supervised & Unsupervised
Reinforcement Learning
Langgraph
Langchain
Crew AI
AutoGen
AWS
CI/CD
Docker
Kubernetes
Github
Human in Loop Agents
LLMOps/MLOps
RAG Evaluation
Langsmith
Deployment
Model Training & Evaluation
Fine-Tuning
SLMs
LoRA
PEFT

100% Placement Assistance & Career Transition

Studying the best Artificial Intelligence course Bangalore is only half the journey; getting the right job is the ultimate goal. Since 2016, BEPEC has enabled over 30,000 career transitions. Our AI training Bangalore consistently delivers measurable career outcomes.

Our placement support includes resume building, LinkedIn optimization, mock interviews, GitHub portfolio reviews, HR rounds preparation, and direct referrals through our 500+ hiring partner network — making BEPEC the best AI institute in Bangalore for placements.

10X Interview Calls: We use a specialized PUSH & PULL strategy with optimized ATS-friendly resumes and LinkedIn profile makeovers.
500+ Hiring Partners: Your profile goes directly to our corporate network, skipping the standard HR queue.
Mock Interviews: Conducted by working AI Engineers and Data Scientists to ensure you can confidently answer the “Why” and “When” behind every architectural choice.

Course Curriculum - AI Course Bangalore

Outcome from BEPEC AI Training with Internship

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

Lab Setup for BEPEC AI Course

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

Foundational Math(Induction Classes)

Vectors, Matrices, Tensors
Matrix operations: Addition, Multiplication, Transposition
Eigenvalues, Eigenvectors, Singular Value Decomposition (SVD)
Dot product, Matrix Inversion

21:23
Functions, Limits, Derivatives
Partial Derivatives & Gradient

02:27
Chain Rule (Backpropagation)
Integration basics

Mastering Python for AI

Introduction to Python
Why Python, Value, Variable, Function, Library
IDE in Python, Different Data Types
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, Generator Expressions, 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
EDA 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
Mastering OpenCV
Working with Beautiful Soup
Hands-On Tensorflow
Hands-On PyTorch
Hands-On LangChain
Hands-On Scikit-Learn Library
Hands-On Scipy

Advanced Data Structures and Algorithms

Introduction to Advanced DSA
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

Hands-On SQL, Projects with Data Warehouse Concepts

What is SQL, RDBMS, and Table structure
Understanding Sprint, Scrum and Agile Project Breakdown in SQL
OLTP vs OLAP
Data Warehousing Concepts

15:42
Data types (INT, VARCHAR, DATE, BOOLEAN, etc.)

29:03
ER Diagrams

18:39
Data Models like Star Schema and Snowflake Schema
DDL vs DML vs DCL vs TCL commands
Basic CRUD Operations

41:21
Different DDL Commands

41:22
Different DML Commands
Upsert Operations
Different DQL Commands
Database Constraints
Aggregate Functions, Date Functions and String Functions
SQL Joins: Inner, Self, Cross, Left, Right and Outer Join
SQL Grouping & Aggregations
SubQueries and Types of SubQueries
Window Functions in SQL

Hands-On Advanced SQL Concepts

Data Integrity & Referential Integrity
Data Normalisation
First & Second Normal Form
Functional Dependency, Transitive Dependency & 3rd Normal Form
Boyce-Codd Normal Form
Denormalization
Temporary Table, Common Table Expression, Recursive CTE
Views, Types of Views and Materialized Views
Stored Functions
Stored Procedures
Triggers in MySQL
Create Events in MySQL
Different Functions in MySQL
Set Operations: Union, Intersect, Except
Transaction Control Language
ACID properties
Index tuning, query rewriting

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

Probability Distributions, 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

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

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

Applied Machine Learning(Basics To Job-Ready)

Types of ML: Supervised, Unsupervised, Reinforcement
Pipeline: Data Cleaning → Feature Selection → Modelling → Evaluation
Bias-Variance Trade-off
Train/Validation/Test splits

06:21
Cross-Validation
Linear Regression (with gradient descent math)
Math behind OLS Technique

04:32
Assumptions of Linear Regression

23:08
Evaluation Metrics: RMSE, MAE, R² Score

15:50
Polynomial Regression
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
Evaluation: Confusion Matrix, Accuracy, Precision, Recall, F1, AUC-ROC
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, Gradient Boosting, XGBoost, LightGBM, CatBoost
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
Clustering: K-Means (Euclidean distance), Hierarchical Clustering
Dimensionality Reduction: PCA (Eigen decomposition), t-SNE
Anomaly Detection
Hyperparameter Tuning: GridSearchCV, RandomSearch
Pipelines with Scikit-learn

Unsupervised Learning with Real-Time Projects

Supervised vs Unsupervised Learning
Fundamentals of Machine Learning Part-1
Fundamentals of Machine Learning Part-2
Insights from Train & Test Accuracy
Associate Rules Part-1
Associate Rules Part-2
Problem Identification & Approach Designing
Associate Rules Scripting
Why to do Dimensionality Reduction? Project Implementation using PCA & T-SNE

Supervised Learning Algorithms

How to Prepare Machine Learning Math for Interviews
Why to use Linear Regression?
What is Linear Regression
Case Study on Linear Regression
Math Behind Linear Regression Part-1
Math Behind Linear Regression Part-2
Math behind OLS Technique
Assumptions of Linear Regression
Evaluation Metrics for Regression Model
Accuracy Improving Techniques
Coding Linear Regression Model
Regularisation Techniques
Why Logistic Regression
Math Behind Logistic Regression
Evaluation Metrics Behind Classification Algorithms
ROC & AUC Curve
Coding Logistic Regression Model
Introduction to Decision Tree
Intuition Behind Decision Tree
Math Behind Decision Tree
Math Behind Decision Tree using GINI
Drawbacks of Decision Tree
Random Forest and Gradient Boosting
Coding Decision Tree, Random Forest, GB
SMOTE Technique to Handle Imbalanced Dataset
Filter Method, Wrapper Method, Embedded Method – Feature Selection Techniques
Coding Feature Selection Techniques
Math Behind KNN
Math Behind SVM
Plotting SVM
Introduction to PCA
Math behind PCA
Coding PCA
AutoML Using Pycaret

Advanced Time Series with Real-Time Projects

Natural Language Processing

What is NLP? Applications in industry
Tokenization, Stopwords, Lemmatization vs Stemming Normalization
Regular Expressions (Regex) in NLP
Bag of Words (BoW)
TF-IDF Vectorization (Term Frequency-Inverse Document Frequency)
Word embeddings: Word2Vec (CBOW, Skip-Gram) GloVe
Cosine Similarity
N-Gram models (Unigram, Bigram, Trigram)
Language Modeling: Probability of a word sequence
Smoothing techniques (Laplace, Good-Turing)
Parts-of-Speech Tagging (Rules + Statistical)
Named Entity Recognition (NER)
Chunking and Chinking
Using spaCy’s NER pipeline

ML Deployment & 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

Advanced Time Series with Real-Time Projects

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

Feed Forward Neural Network Coding

Foundations of Generative AI

Large Language Models (LLMs)

Small Language Models (SLMs)

Generative AI, Prompt Engineering using LangChains

What is Generative AI? Use cases in text, image, code, and conversation
Foundation Models: GPT, Claude, PaLM, Gemini, Mistral, LLaMA
Introduction to Lang Chains
Using LLMs using Lang Chains
What is Prompt Engineering
Prompt Engineering Principles
Zero-shot, Few-shot, Chain-of-Thought (CoT)
Best Way to Improve LLMs Accuracy
Doing Prompt Engineering using Lang Chains
PromptTemplate best practices with LangChain
Using ChatPromptTemplate, SystemMessagePrompt, HumanMessagePrompt
Prompt evaluation: temperature, top_p, stop, max_tokens

LangChain Framework

Amazon Bedrock & GenAI on Cloud

Retrieval-Augmented Generation (RAG) with LangChain

Why RAG? Token limits, context injection
Vector Embeddings: OpenAI, HuggingFace, Cohere
Vector DBs: FAISS, Pinecone, Chroma, Weaviate, Milvus
LangChain’s RetrievalQA Chain
Chunking strategies and TextSplitter
Hybrid search: Dense + Metadata filtering
SequentialChain vs SimpleSequentialChain
RunnablePassthrough, RunnableLambda (LCEL)
Creating Tool abstractions with @tool decorator
Integrating APIs (e.g., SerpAPI, Weather, Calculator)
Output parsers: StrOutputParser, StructuredOutputParser

Agentic AI

LangGraph – Multi-Agent DAG Framework

Multi-Agent Systems

LangFlow – Visual Low-Code Interface

Agentic AI – Build Autonomous Agents with LangChain

Model Context Protocol(MCP)

Model Context Protocol (MCP) and Multi-Agent Architectures

n8n for Agent Workflow Automation

LLMS: Hugging Face

Fine-Tuning and Pretraining Language Models

LLMOps & Production Deployment

Full Stack Application + Deployment(LLMOps)

Advanced Deep Learning

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

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

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

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, NLU, NLG

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

Topic Modelling

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

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

Natural Language Generation Using GPT

Mastering T5

Vision Transformers

Autoencoders

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

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

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

AI & Generative AI Internship Projects in BEPEC

Financial Chatbot using RAG, Fine-Tuned LLM, Vector Database and Prompt Engineering using AWS
RAG Architecture : Using Open AI, Gemini, LangChain, Amazon BedRock & Solara
Multi-Agent System: HR Screening Agent using RAG Architecture, Agentic AI, Amazon BedRock, MCP, LangGraph, LangChain
Fine-Tuning LLM using Hugging Face
Build End-to-End “n8n” Workflow Automation
Build End-to-End: Custom LLM Fine-Tuned for Customer Support
AI Project with Amazon S3, Snow Flake, Spark & Snow Park with CI/CD Pipeline
End-to-End Machine Learning Project Using Spark & Microsoft Fabric
AGILE CRISP: End-to-End Machine Learning Project Pipeline
MLOps Pipeline: End-to-End AI/ML Lifecycle using MLOps
Streaming Analytics: End-to-End NLP Project
End-to-End Computer Vision Project with Deployment
Image Based Generative AI Project using Diffusion Models

Pyspark: BigData Processing(Smart-Board Access)

Introduction to Spark
Various divisions in Spark
Python with Spark
Pyspark for MLLib
Spark DataFrames
Spark Operations
Grouping, Aggregating and Joining with Spark
Introduction to Machine Learning with PySpark
Machine Learning Pipeline with Spark
Frequent Pattern Mining and Statistics
Feature Transformation with Spark
Data Cleaning & Pre-Processing with Spark
NLP with Pyspark
Spark Structured Streaming
Streaming ML Solutions

MongoDB Mastering(Smart-Board Access)

Introduction to Mongo DB
Installation of MongoDB
Basics Operations of MongoDB
Create Operations in MongoDB
Update Operations in MongoDB
Read Operations in MongoDB
Delete Operations in MongoDB
Query & Projection Operations
Python with MongoDB
Spark with MongoDB

R Programming(Smart-Board Access)

Installation of R Studio

19:42
Basics of R Studio

39:40
Data Visualisation with R Studio

33:25
Data Modelling with R Studio

13:53
Data Joining with R Studio

09:45
Data Visualisation with GGPlot2

11:03
Data Preparation with R Studio

09:39
Data Cleaning with Regexp

34:30
Data Visualisation with Plotly

44:40
test

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

Artificial Intelligence Certification Course

Our career-focused Artificial Intelligence 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 real-time projects 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.

Student Success Stories & Reviews

Frequently Asked Questions

How are BEPEC's AI projects different from other Bangalore institutes?

Interviews focus on ROI (Return on Investment). While other learners build basic models, BEPEC trains you to build Client-Level Projects with clear ROI. You will learn to explain why you chose a specific RAG architecture or Multi-Agent system over traditional methods.

Do I need to know Python before joining this AI course in Bangalore?

No! Our curriculum is built for both beginners and experienced professionals. We teach Python from the absolute basics, including local system setup and environment configuration.

Who is eligible for AI course?

Completing Any Degree or Engineering with 60-70% Aggregate.

Willingness to learn Programming
Willingness to learn Math behind AI
Minimum 2Hours of Time Commitment per day

Is Github Portfolio Important? To Crack AI Job?

Yes, Github Portfolio is important. Recruiters are looking for Github Links to check your practical knowledge.

In BEPEC AI Course, we help you build Github & LinkedIn Portfolio

Can I get a fresher job in AI and Data Science?

With BEPEC’s “Experience Building” approach and real-time project portfolio, you won’t be treated as an absolute fresher. You will enter the market with practical knowledge, attracting better designations and higher salary hikes.

How BEPEC Help me Get Job?

BEPEC Projects & BEPEC Strategy helps you get 10X Interview Calls
BEPEC Shares your Resume with 150+ Hiring Partners
BEPEC ATS Resume with Job Portal and LinkedIn Optimization gets you more interview calls with PUSH & PULL Strategy
BEPEC Mock Interviews helps you speak in interviews with confidence and you can crack multiple offers!

Who are the Instructors?

Our Instructors are working AI Engineers, AI Researchers, AI Architects, Head of AI, Data Scientist who are working and delivering trainings as freelancers.

Head Trainer Kanth:

- Kanth Worked as a Principal Data Scientist with proficiency in Machine learning, Deep Learning, Generative AI, and LLMs, with around 12+ years of involvement in conveying end-to-end project pipelines using AGILE CRISP, DataOps, Big Data Streaming Pipelines, and MLOps Pipelines
- Kanth is an AI CoE and Six Sigma Certified. He is an AI Consultant for companies like Nokia, EY, Cognizant, BMW, DU Telecom etc. Kanth delivered AI Solutions using AI Software Stack.
- Kanth has experience in delivering customized training on statistics, cloud computing, machine learning, deep learning, Generative AI, Data Engineering, Data Analytics, Data Science, and Reinforcement learning.
- Delivered Training Across Dubai, Malaysia, Singapore, South Africa, Sudan etc.
- Helped various organisations to implement Agentic AI & Gen AI using AI Software Stack & made them achieve tremendous financial gains
- Experience in Delivering 100+ corporate training sessions for working Data Scientists and AI Engineers to Upskill them for Future Projects.
- Experience in delivering 50+ PMP Programs, ITIL Programs, Agile Programs, etc
- India – EY, Infosys, TechMahindra, ExlSolutions, Suzlon, Wipro, TCS, CSC, HCL, HP, Deloitte, IIT-Bhubaneswar, KIIT–Bhubaneswar, R&D – Hyundai
- Corporate training across different location like Dubai(Etisalat), Malaysia ( X-Fab), Canada(X-Axis), Singapore(DBS Bank)
- Colleges – Nearly 400+ Colleges all over India
on “Agentic AI and Future of Autonomous Systems.

For Corporate Training Requirement:
1. We Customise the Training Based on Your Requirement
2. We Help Learners to Build POCs with Our Implementation Based Approach