Best Artificial Intelligence Course Bangalore (2026)
The only Artificial Intelligence course Bangalore covering Agentic AI, LangGraph, AutoGen, CrewAI, SLMs, Prompt Engineering, RAG & MLOps. Join 10,000+ alumni in Koramangala & Marathahalli. 10+ Years of Excellence in [Placement Support]
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
19th Feb, 2026
Time Commitment
1.5-2Hours/Day
Program Duration
6 Months
Learning Format
Live Classes + Experience Building
We Help You Make Career Switch into AI
Instructors Real-World AI Projects Experience
Guided Hands-On Job-Switch program designed by AI Solutions Architect & Generative AI & Agentic AI Consultant Rajeev Kanth.
This program bridges the gap between experimental AI and production-grade Agentic Systems.
Master the modern 2026 stack, LangGraph, CrewAI, Groq, and Vector DBs, while integrating with enterprise pipelines like Databricks, Kafka, and Kubernetes. Build scalable, governed, and self-improving AI agents.
Real-World Projects Developed by Rajeev Kanth
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.
About Artificial Intelligence Course Bangalore
Build a portfolio on Generative AI, Agentic AI in the booming field of AI with BEPEC’s industry-leading Artificial Intelligence Course in Bangalore.
Designed for both beginners and experienced professionals, this “Experience Building” program goes beyond theory to offer hands-on training in Generative AI, Agentic AI, MLOps, and Deep Learning.
Whether you are looking to switch careers or upskill for a higher salary, our comprehensive curriculum ensures you are job-ready for the top tech companies in Bangalore and beyond.
Our Artificial Intelligence Course covers the entire AI lifecycle, from foundational mathematics to deploying scalable Gen AI agents.
Core Fundamentals: Python, SQL, Advanced DSA, and Applied Statistics.
Machine Learning & Deep Learning: Supervised/Unsupervised Learning, CNNs, RNNs, and Transformers.
Generative AI & LLMs: Master RAG (Retrieval-Augmented Generation), Prompt Engineering, Fine-Tuning SLMs/LLMs, and Vector Databases.
Agentic AI: Build autonomous systems using LangChain, LangGraph, CrewAI, and AutoGen.
MLOps & Deployment: Gain expertise in Docker, Kubernetes, CI/CD pipelines, and Cloud deployment (AWS/Azure/GCP).
Computer Vision & NLP: Hands-on projects with OpenCV, SpaCy, BERT, and GPT models.
Why Choose BEPEC for AI Training in Bangalore?
Finding the right AI training institute in Bangalore can be challenging. BEPEC stands out by offering a practical, project-based learning approach that mirrors real-world industry demands.
Experience Building: Don’t just learn; build a portfolio. We focus on creating live Proof of Concepts (POCs) that you can showcase on LinkedIn and your resume.
Live Instructor-Led Training: Learn directly from industry experts with 12+ years of experience.
Career Switch Focus: tailored for professionals seeking a 60-100% salary hike and a smooth transition into Data Science and AI roles.
Artificial Intelligence Course Real-Projects with Internship
It detects schema drift or “bad data” events in milliseconds and hot-fixes the transformation logic before the downstream warehouse is polluted.
Instead of a static CRM, this agent uses MCP to “live-browse” a prospect’s recent LinkedIn activity, company news, and financial reports to generate personalized outreach.
A system that goes beyond keyword matching to simulate an “Initial Technical Screen” through an agentic conversation with the candidate’s data.
A real-time troubleshooting agent for cloud infrastructure that “talks” to your Kubernetes cluster and Slack.
A hyper-personalized recommendation engine for doctors that suggests drugs based on a patient’s real-time genetic profile and live drug-interaction databases.
Instead of blindly trusting whatever documents the vector database finds, this agent grades the quality of the documents and acts accordingly
“The user said it’s broken.” The RAG system doesn’t know what “it” refers to because that info was in the previous chunk.
Course Curriculum - AI Course in Bangalore
- Introduction to Artificial Intelligence and its real-world impact
- Evolution of AI from rule-based systems to deep learning
- Overview of Machine Learning, Deep Learning, and core algorithms
- Understanding Generative AI and Foundation Models
- Working with Large Language Models (LLMs) and Prompt Engineering
- Exploring Retrieval-Augmented Generation (RAG) for contextual intelligence
- Introduction to Agentic AI and autonomous intelligent systems
- Key components of agents — Memory, Tools, Planning, and Decision Nodes
- Overview of LangChain, LangGraph, AutoGen, CrewAI, and Semantic Kernel
- Multi-Agent collaboration frameworks and real-world applications in automation
- Demonstration of Planner–Executor–Evaluator architecture in multi-agent systems
- Integration of Generative AI with Agentic AI for autonomous decision-making
- Career pathways and skill roadmap in AI, Gen AI, and Agentic AI
- Introduction to Python
- Why Python, Value, Variable, Function, Library [Roadmap on Python]
- IDE in Python, Different Data Types
- List, Tuple, Set & Dictionary Overview
- Different List Methods
- Different Tuple Methods
- Set & Frozenset
- Dictionary & String Manipulations
- Overview on Loops, If Statements, UDFs, Escape Sequences, Lambda
- Types of Operators, Conditional Statements
- While Loop, List Comprehension, Break, Continue, Arguments
- Functions, Escape Sequences, Lambda Functions
- Hackathon-1
- Iterators, Decorators & Generators
- Modules in Python
- Creating Custom Python Libraries
- Lambda, Map, Filter, Reduce
- Exception Handling
- File Handling
- Regular Expressions (Regex)
- Web Scraping Basics
- Introduction to OOPS
- Instance Variable, Class Variable, Class Method
- Association vs Composition & Aggregation
- Oops Concept
- Encapsulation, Inheritance
- Polymorphism, Method Overloading, Method Overriding
- Introduction to Pandas
- Data Analysis using Pandas
- Introduction to Numpy
- Different Numpy Commands
- Introduction to Data Visualisation
- Data Visualisation using Matplotlib
- Data Visualisation using Seaborn
- Data Visualisation using Plotly
- Why Data Cleaning?
- Data Cleaning with Sklearn & pandas
- Regular Expression Basics
- Mastering Langchains
- Mastering Tensorflow
- Mastering PyTorch
- Mastering Langgraph
- Mastering Crew AI
- Mastering Streamlit
- Mastering Langsmith
- 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
Pandas DataFrames for Coding Interviews
Data Cleaning and Imputation Logic
GroupBy and Aggregation Coding
Merging, Joining, and Concatenating DataFrames
Reshaping Data (Melt, Pivot)
NumPy Array Broadcasting
Vectorized Operations vs Loops
Handling DateTime Objects in Python
String Manipulation with Regex
- What is SQL, RDBMS, and Table Structure
- Understanding Sprint, Scrum and Agile Project Breakdown in SQL
- OLTP vs OLAP
- Data Warehousing Concepts
- Data types (INT, VARCHAR, DATE, BOOLEAN, etc.)
- ER Diagrams
- 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
- Data Integrity & Referential Integrity
- Data Normalisation
- First & Second Normal Form
- Functional Dependency & Transitive Dependency
- Boyce Codd Normal Form
- Denormalization
- Temporary Tables, CTE, Recursive CTE
- When to Use Temporary Table, CTE, Recursive CTE
- SubQuery in MySQL
- Views in MYSQL
- Stored Functions
- Stored Procedures
- Triggers in MySQL
- Create Events
- In-depth DDL Commands
- Different Functions in MySQL
- 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
- Basics of Statistics
- Central Limit Theorem, Normal Distribution, Skewness
- Real-Time Project Demonstration on EDA with Real-World Project
- End-to-End Statistics using Excel
- 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?
- Chebyshev’s, Log, Power Law, Q-Q, CLT
- 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 Methods
- 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
- Embedded Methods: Regularisation
- Embedded Method: Elastic Net Regression
- Embedded Method: Decision Tree
- Other Techniques
- Outlier Treatment
- Over Sampling Technique
- Under Sampling Techniques
- Types of ML: Supervised, Unsupervised, Reinforcement
- Pipeline: Data Cleaning → Feature Selection → Modelling → Evaluation
- Bias-Variance Trade-off
- Train/Validation/Test Splits
- Cross-Validation
- Linear Regression (with gradient descent math)
- Math behind OLS Technique
- Assumptions of Linear Regression
- Evaluation Metrics: RMSE, MAE, R² Score
- Polynomial Regression
- Coding Linear Regression Model
- Regularisation Techniques
- Why Logistic Regression
- Math Behind Logistic Regression
- Evaluation Metrics Behind Classification Algorithms
- Evaluation: Confusion Matrix, Accuracy, Precision, Recall, F1, AUC-ROC
- 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, Gradient Boosting, XGBoost, LightGBM, CatBoost
- 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 — 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
- 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
- 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
- Additional Topics
- Introduction to PCA
- Math behind PCA
- Coding PCA
- AutoML Using Pycaret
- 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
- End-to-End Deployment Using MLOps & Streamlit
- Setup Virtual Environment
- Docker Installation
- How to Activate Virtual Environment
- Docker Desktop
- Regression Model using Pycaret
- Regression Model using Pycaret Part-2
- Interpretability of Model using SHAP
- What is SHAP
- Application Development using Gradio
- Creating API using FASTAPI
- Creating Docker Image
- Model Versioning using Pycaret & MLFlow
- End-to-End ML Proof of Concept
- 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
- Setting up AWS Sagemaker & Deploying Simple ML Model
- AIOps with AWS Sagemaker Studio
- 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
- Introduction to CNN & Why CNN Algorithm
- What is Object Detection & Object Classification
- Intuitive Understanding Behind CN
- 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 Fre-Trained Models for Object Classification
- Deploying Image Classification Model in AWS
- Project Demonstration. Image Classification Project
- 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
- 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
- Introduction to Transfer Learning
- Headless & With Head Transfer Learning
- Object Detection using YOLO
- Object Detection using Open CV
- Transfer Learning with Tensorflow Hub
- 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
- Project Demonstration: Resume Scoring
- 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
- What is Generative AI? Applications and Industry Use-Cases
- Evolution: GANs → VAEs → Transformers → Diffusion → LLMs → MoEs
- Key Architectures: GPT, BERT, Diffusion, UNet, U-ViT
- Metrics: FID, BLEU, ROUGE, perplexity, hallucination rate
- What is Generative AI? Use cases in text, image, code, and conversation
- Foundation Models: GPT, Claude, PaLM, Gemini, Mistral, LLaMA
- Introduction to LangChains
- 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,
- Overview of LangChain and Ecosystem
- Components: Chains, Agents, Tools, Memory, Prompts
- Integration with OpenAI, Anthropic, Cohere, HuggingFace
- Use Cases: Doc Chatbot, PDF QA, RAG Chatbots, Tools-based Agents
- Custom Chains and Agents
- What is Amazon Bedrock?
- Using Claude, Titan, and Meta Models via Bedrock
- Prompt Chaining, Guardrails, Evaluators in Bedrock
- Comparison with Vertex AI (Google), Azure AI Studio
- Custom Fine-Tuning and Hosting on Bedrock
- 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
- What is Agentic AI?
- Agent Architecture: Planning → Tool Use → Memory → Reflection
- Tools: AutoGPT, BabyAGI, SuperAgent, CrewAI, AgentLabs
- Key Capabilities: Decision Trees, Recursive Planning, Dynamic Memory
- Toolformer & Tool Use via Function Calling
- Types of agents in LangChain: ReAct, ConversationalReAct, Plan-and-Execute, OpenAIFunctionsAgent
- Agent memory (ConversationBufferMemory, SummaryMemory)
- Task Decomposition and Planning
- Use Cases: Code generation, Auto-documentation, Research Assistants
- What is LangGraph? (Directed Acyclic Graphs for LLM Workflows)
- Graph Node Design: Single-Agent vs Multi-Agent Nodes
- Persistent Memory and State Sharing Between Agents
- Use Case: Build a Modular Multi-Agent PDF Analyst
- Integration with OpenAI & Anthropic Models
- What are Multi-Agent Systems (MAS)?
- Types: Cooperative, Competitive, Mixed-Mode Agents
- Examples: ChatDev, AgentVerse, CAMEL, OpenDevin
- Communication Protocols between Agents
- Real-world Multi-Agent Workflow Simulations
- Definition & Role of MCP in LLM systems
- Standardising LLM Input/Output via JSON Schemas
- Tool Usage: Tool Metadata, Function Signatures
- Comparison: MCP vs LangChain Schemas
- What is Model Context Protocol (MCP)
- Context-aware decision making
- Prompt-as-function vs Function-as-prompt
- Designing structured workflows (LangGraph overview)
- Communication between agents
- Build an MCP-based RAG agent
- Implement agent-to-agent communication where one retrieves, another summarizes, and a third decides actions
- Difference between Pretraining, Fine-Tuning, Instruction Tuning
- Fine-Tuning Open Source Models (LLaMA, Mistral, Falcon)
- PEFT Techniques: LoRA (Low-Rank Adaptation) Adapters, Prefix-Tuning
- Tools: HuggingFace Transformers, PEFT, DeepSpeed, bitsandbytes
- Model Lifecycle: Dev, Eval, Test, Deploy, Monitor
- LLMOps Stack: LangSmith, PromptLayer, WandB, Evidently, TruLens
- Logging, Prompt Versioning, Evaluation Metrics
- CI/CD for Prompts and Chains
- RLHF, Feedback Loops, Guardrails
- Combining RAG, Agentic AI, and Fine-tuned models
- Deploying LangChain applications on: Streamlit, Solara, FastAPI, Gradio
- CI/CD Pipelines for LLMOps
- Monitoring and Cost Management (OpenAI + LangSmith + Tracing)
- Build an End-to-End Custom Knowledge Assistant:
- Ingest PDFs/Docs → Chunk and Embed → Vector DB Query using Conversational Agent (RAG + Tools + Planner) → Deploy using Solara
What is the fee for an AI course in Bangalore?
Get Group Discount & Corporate Training Discounts
Google Ratings & Trustpilot Reviews!
“I have enrolled for the Data Science Career Transition Program through Bepec Kanth explains how to apply all the concepts in real world. Along with developing domain knowledge he is also helping to enhance our professional skills to face real world challenges as data scientist... What ? Why? Where ?.....3 words most important”
SREE SAI KONDLE
“First of all, I clearly got to know the role of DA/DS ,previously joined to one institute but i didnt get enough knowledge + no real time projects So, I joined here in oct batch everything is so perfect the way Kanth sir teaches and it is more like one should be consistent in learning. When it comes it support team they will always help u at ur queries will update about placement once i got placed.”
Harshini
“Hi I am grateful to Kanth and his team who worked very hard for career transition program into data science and AI field, the program was very smooth starting from the scratch to pro level. As I belong to mechanical field even I grabbed the insights very easily I think one must go for their program without any hesitations. I hope I would become principal data scientist one day. Thank you Kanth and his team”
Mayur Thakare
Artificial Intelligence(AI) Exam & Certification in Bangalore
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.
Frequently Asked Questions
In This Course mainly focuses on transforming learners into professionals with confidence to crack any interview by teaching (What(Theory), How(Coding) and Why, When(Projects/Portfolio)
- We Teach What, How, Why and When behind Every Topic
- Interviewers focus on Why and When Behind Your End-to-End Projects in your Resume
- Example Why RAG Project, When to use RAG, vs Prompt Engineering, Why LLMs, Why Not SLMs, Why this billing strategy? Why this model in production? Why LLMOps, When LLMOps
- Why Multi-Agents, When to use Multi-Agents with Human in loop? ( they are failing in interviews)
Recruiters focus on your projects with ROI(return on investment) (Most of the learners dont have ROI based Projects) + They ask Why and When?(Most of the learners Fail Here)
In BEPEC, We give Projects with ROI by making you to work on our Client Projects as Interns/Freelancers
Yes, This Course is Designed for Beginners. We start from fundamentals to Solid Level Understanding with End-to-End projects on Python, SQL, Statistics, Snowflake, Machine learning, Deep Learning, Generative AI, Agentic AI, MCP, Langchains, Langgraphs, Azure, AWS Bedrock, LLMOps, RAG, Prompt Engineering, Fine-Tuning, SLMs, LLMs and Pre-Training
Yes, you can place BEPEC Projects in Resume & LinkedIn. Lot of PG Learners, Freshers and Working professionals crack jobs by placing our Projects in Resume.
No, We Teach Python from Basics. We guide you on installation of Python in Your System
No, With BEPEC Course you will get placed with Salary Hike and Better Designation in AI, Gen AI & Data Science.
To Learn How? Book 1:1 Call with Trainer
- 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!
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.