2026 Batch Now Open
New batch starting July 2026 at BTM Layout  ·  Only ₹40,000 for 6.5 months  ·  Book free demo →
BTM Layout, Bengaluru · 2026 Cohort

Master Data Science,
Machine Learning

& Artificial Intelligence

India's most comprehensive 6.5-month program — 5 modules, 50+ chapters of ML/AI/DL/NLP, plus 2026 additions in GenAI, LLMs & Agentic AI. Trained by working industry professionals. Placed in 150+ companies.

Core Python
Advanced Python
SQL
Statistics
ML · AI · DL · NLP
GenAI & LLMs 2026
★★★★★
4.9 · 22,514 reviews
3,500+ trained
98% placement
150+ companies
Total Course Fee
₹40,000
One-time · All-inclusive · No hidden fees
Duration6.5 Mo.
5 Modules Included
01
Core Python
02
Advanced Python
03
SQL Basic & Advanced
04
Applied Business Statistics
05
ML · AI · Deep Learning · NLP
Live Projects
Resume Workshop
Mock Interviews
Placement Support
Cloud Lab Access
Certificate
Book Free Demo Class
0% EMI available · Scholarships for deserving students
3500+
Students Trained
₹40K
Course Fee
6.5
Months Duration
98%
Placement Rate
150+
Hiring Partners
Google Ad — 728×90 Leaderboard
5 Core Modules
Everything You'll Master

A carefully sequenced curriculum — from Python fundamentals to production-grade AI systems. Every module builds on the last.

Module 01
🐍

Core Python

Start from zero — master Python's syntax, data structures, OOP, and file handling across 23 comprehensive chapters.

Variables, Loops, Functions
OOP — Classes & Objects
Decorators & Generators
File I/O · Modules · Packages
Error Handling & Logging
Module 02

Advanced Python

The data science stack — NumPy, Pandas, Matplotlib, Seaborn for real-world data manipulation and rich visualisation.

NumPy Array Operations
Pandas — DataFrames & Wrangling
Matplotlib — 15+ Chart Types
Seaborn Statistical Plots
Regular Expressions
Module 03
🗄️

SQL Basic & Advanced

Master relational databases — from fundamental queries to advanced window functions and business analytics projects using PostgreSQL.

PostgreSQL Fundamentals
Joins, Aggregations, Subqueries
Window Functions & CTEs
Database Design
SQL for Business Intelligence
Module 04
📊

Applied Business Statistics

Build the mathematical foundation every data scientist needs — probability, distributions, and hypothesis testing across 8 chapters.

Descriptive Statistics
Probability & Bayes Theorem
Statistical Distributions
Sampling & Estimation
Hypothesis Testing
Module 05
🤖

ML · AI · DL · NLP

The crown jewel — supervised and unsupervised ML, deep neural networks, NLP, and cutting-edge Generative AI with 50+ chapters.

Machine Learning Algorithms
Neural Networks & Deep Learning
NLP & Transformers
Generative AI & LLMs (2026)
Agentic AI & MLOps
Why Choose Us
Why InfoDrafters Stands Apart

We don't just teach concepts — we engineer careers. Here's what makes us the first choice in Bangalore.

01

Industry-Expert Trainers

Every instructor is a working professional from a top tech company — not a full-time academic. They bring current, real-world context to every class, including tools actively used in 2026.

02

Hands-On from Day One

Unlimited access to our cloud-based Data Science lab. You work on real datasets from day one — no toy problems. Our capstone projects go straight into your portfolio and impress hiring managers.

03

Career-First Program

Resume building, LinkedIn optimisation, mock technical interviews, GD training, and direct placement support with 150+ hiring companies. We work until you are placed.

04

Curriculum Updated Every 6 Months

The AI landscape shifts fast. We update our syllabus continuously — GenAI, LLMs, and Agentic AI are fully integrated into the 2026 curriculum so you learn what companies actually need right now.

05

Globally Recognised Certificate

Our certificates are trusted by 150+ companies. Add it to your LinkedIn, attach it to job applications, and stand out in every shortlist. The InfoDrafters name carries weight in Bangalore's tech ecosystem.

06

Flexible Learning Formats

Online live, in-person at BTM Layout, or hybrid. Morning, evening, and weekend batches available. We fit around your schedule — whether you're a fresher, working professional, or career changer.

New in 2026
What's Added This Year

AI evolves every 6 months. So does our syllabus. Here's what's brand new or significantly updated for 2026 students.

New 2026

Generative AI (GenAI)

Work hands-on with GPT-4o, Claude 3.5, and Gemini 1.5. Build text, image, and multimodal applications. Master zero-shot, few-shot, chain-of-thought, and ReAct prompt engineering.

New 2026

Large Language Models (LLMs)

Understand transformer architecture, fine-tune open-source LLMs (LLaMA 3, Mistral) with LoRA & QLoRA. Deploy models locally with Ollama and at scale on cloud infrastructure.

New 2026

Agentic AI Systems

Build autonomous AI agents with LangChain, LangGraph, and AutoGen. Design multi-agent workflows with memory, tool use, and human-in-the-loop logic for real business automation.

New 2026

RAG & Vector Databases

Build Retrieval-Augmented Generation systems. Use Pinecone, ChromaDB, and FAISS for semantic search. Create intelligent document Q&A bots that work on your own private data.

New 2026

MLOps & AI Deployment

Deploy ML and AI models with FastAPI and Docker. Set up CI/CD pipelines with GitHub Actions. Monitor model performance with MLflow. Deploy to AWS SageMaker and Google Vertex AI.

Updated

Power BI Copilot 2026

Use Power BI's AI-powered Copilot to generate reports from natural language. Build real-time dashboards, automated KPI alerts, and publish to Power BI Service for stakeholder review.

Updated

Cloud AI (AWS + GCP)

Train and serve models on AWS SageMaker and Google Vertex AI. Use AWS Bedrock for GenAI APIs at scale. Learn cloud cost optimisation and security for production AI workloads.

New 2026

AI Ethics & Responsible AI

Understand bias, fairness, and explainability (SHAP, LIME). Navigate AI governance frameworks and data privacy regulations — essential knowledge for every AI professional in 2026.

Google Ad — In-Article / 336×280
Full Curriculum
Detailed Syllabus — 6.5 Months

Every topic you'll cover, structured module by module. Click any section to expand.

  • Python Basics & Syntax
    • Variables, data types, operators
    • Input/output, arithmetic, comparison, logical, bitwise operators
    • Type casting, naming conventions
  • Control Flow & Loops
    • IF / ELIF / ELSE decision structures
    • FOR, WHILE, nested loops
    • BREAK, CONTINUE, PASS
  • Functions
    • Built-in & user-defined functions
    • Lambda, filter, map, reduce
    • Default, keyword & variable-length args
    • Recursive functions, closures
  • Data Structures
    • Lists — slicing, comprehension, sorting
    • Tuples, Sets, Dictionaries in depth
    • Strings — methods, formatting, f-strings
  • OOP — Classes & Objects
    • Class, object, constructor, self
    • Instance, static, class variables & methods
    • Inheritance, polymorphism, encapsulation
    • Getters/setters, destructors, magic methods
  • Advanced Concepts
    • Decorators & decorator chaining
    • Generators & yield
    • Regular expressions with re module
    • Comprehensions — list, dict, set
  • Modules, Packages & File Handling
    • Importing modules, aliasing, reloading
    • CSV, JSON, binary files, Pickle
    • Directory handling with os, pathlib
    • Exception handling & custom exceptions
    • Logging — levels, formatters, handlers
  • NumPy
    • Arrays, broadcasting, multi-dimensional operations
    • Random module, indexing, slicing, reshaping
    • Mathematical & statistical functions
  • Pandas
    • Series and DataFrames in depth
    • Merging, joining, groupby, pivoting, melting
    • Handling missing values, data filtration
    • Multi-level indexing, aggregation, apply/map
  • Matplotlib
    • Line, bar, scatter, histogram, area, box, pie charts
    • Subplots, figure size, DPI, annotations, legends
    • Time series visualisation, styling
  • Seaborn
    • displot, jointplot, pairplot, heatmap
    • boxplot, violinplot, swarmplot, stripplot
    • FacetGrid, lmplot, clustermap
    • Color palettes, themes, context settings
  • SQL Fundamentals
    • SELECT, WHERE, DISTINCT, COUNT, LIMIT, ORDER BY
    • BETWEEN, IN, LIKE, NULL handling, aliases
  • Joins & Aggregations
    • INNER, LEFT, RIGHT, FULL OUTER JOIN, UNION
    • GROUP BY, HAVING, MIN, MAX, SUM, AVG
  • Advanced SQL
    • Window functions — ROW_NUMBER, RANK, DENSE_RANK
    • LEAD, LAG, NTILE, PERCENT_RANK, FIRST/LAST_VALUE
    • Subqueries, correlated subqueries, self-joins
    • CTEs — definition, joining, recursive CTEs
    • CASE expressions, conditional aggregation
  • Database Design
    • Data types, primary & foreign keys, constraints
    • VIEWS, CHECK, NOT NULL, UNIQUE, DEFAULT
    • INSERT, UPDATE, DELETE, ALTER, DROP, TRUNCATE
  • PostgreSQL with Python
    • psycopg2 connection & cursor management
    • Executing parameterised queries from Python
  • SQL for Business Intelligence
    • Customer behaviour & cohort analysis
    • Order status & cross-selling pattern analysis
    • Profit & Loss analysis project
    • Customer lifetime value prediction queries
    • Sales funnel and conversion rate analysis
  • Descriptive Statistics
    • Mean, median, mode, percentiles, quartiles
    • Variance, standard deviation, IQR
    • Skewness, kurtosis, coefficient of variation
  • Data Visualisation for Stats
    • Histograms, frequency polygons, cumulative frequency
    • Box plots, cross-tabulation, qualitative data charts
  • Probability Theory
    • Classical, empirical & subjective probability
    • Conditional probability, Bayes theorem
    • Independent & dependent events, permutations, combinations
  • Probability Distributions
    • Discrete — Binomial, Poisson, Hypergeometric
    • Continuous — Normal, Uniform, Exponential
    • Central Limit Theorem
  • Sampling & Estimation
    • Random vs non-random sampling methods
    • Confidence intervals using z and t statistics
    • Estimating population proportion & sample size
  • Hypothesis Testing
    • Null & alternate hypothesis, two-tailed & one-tailed tests
    • z-test, t-test, chi-square, ANOVA
    • Type I & II errors, p-values, effect size, statistical power
  • ML Foundations
    • Supervised, unsupervised, reinforcement learning
    • Data preprocessing, feature engineering, scaling, encoding
    • Train/test split, cross-validation (k-fold, stratified)
  • Regression
    • Simple & multiple linear regression
    • Polynomial, ridge (L2), lasso (L1), elastic net
  • Classification
    • Logistic regression, decision trees, random forest
    • SVM (linear & kernel), KNN, Naive Bayes
  • Unsupervised Learning
    • K-Means, DBSCAN, hierarchical clustering
    • PCA, t-SNE, UMAP — dimensionality reduction
    • Anomaly detection — Isolation Forest, One-Class SVM
  • Ensemble Methods
    • Bagging, boosting, stacking
    • XGBoost, LightGBM, CatBoost — in-depth
  • Model Evaluation & Tuning
    • Confusion matrix, ROC-AUC, F1, Precision, Recall
    • RMSE, MAE, R² for regression
    • Grid search, random search, Optuna (Bayesian optimisation)
    • Feature importance, SHAP values, LIME
  • Neural Network Foundations
    • Perceptrons, activation functions (ReLU, Sigmoid, Tanh, GELU)
    • Forward & backpropagation, vanishing gradient problem
    • Optimisers — Adam, SGD, RMSProp, AdaGrad
    • Batch normalisation, dropout, L1/L2 regularisation
  • CNNs — Computer Vision
    • Convolutional & pooling layers, feature maps, receptive fields
    • Image classification, object detection (YOLO)
    • Transfer learning — ResNet, VGG, EfficientNet, MobileNet
  • RNNs, LSTMs & GRUs
    • Sequential data, time series with deep learning
    • LSTM & GRU architecture in detail
  • Transformers Architecture
    • Self-attention, multi-head attention mechanism
    • Positional encoding, encoder-decoder structure
    • BERT, ViT — vision transformers
  • TensorFlow & PyTorch
    • Model building, training loops, GPU utilisation
    • TensorBoard, model checkpointing
    • Exporting models — TFSaved Model, ONNX, TorchScript
  • NLP Fundamentals
    • Tokenisation, stemming, lemmatisation
    • POS tagging, NER, chunking, stop words, n-grams
  • Text Vectorisation
    • Bag of Words, TF-IDF
    • Word2Vec (CBOW & Skip-gram), GloVe, FastText
    • Sentence embeddings — SBERT, Universal Sentence Encoder
  • NLP Applications
    • Sentiment analysis, text classification
    • Information extraction, topic modelling (LDA)
    • Document summarisation (extractive & abstractive)
    • Question-answering systems
  • Pre-trained Models & Hugging Face
    • BERT, RoBERTa, DistilBERT, ELECTRA
    • Hugging Face Transformers pipeline API
    • Fine-tuning BERT on custom classification datasets
  • spaCy & NLTK
    • Custom NLP pipelines, entity ruler, custom components
    • Text preprocessing workflows at scale
  • Generative AI Overview
    • GANs, VAEs, diffusion models
    • GPT-1 to GPT-4o, Gemini 1.5, Claude 3.5 — evolution
  • Prompt Engineering
    • Zero-shot, few-shot, chain-of-thought prompting
    • ReAct prompting, structured outputs, system prompts
    • Temperature, top-p, top-k — parameter control
  • Working with LLM APIs
    • OpenAI API — GPT-4o, function calling, streaming
    • Anthropic Claude API, Google Gemini API
    • Running LLaMA 3 & Mistral locally using Ollama
  • Fine-tuning LLMs
    • LoRA, QLoRA — parameter-efficient fine-tuning
    • RLHF, DPO, instruction tuning techniques
    • Evaluating fine-tuned models — BLEU, ROUGE, perplexity
  • Multimodal AI
    • Vision-language models — GPT-4o vision, LLaVA
    • Image generation — DALL-E 3, Stable Diffusion XL
    • Audio & video AI introduction
  • LangChain Framework
    • Chains, memory, LangChain Expression Language (LCEL)
    • Tool use, custom tools, callbacks, output parsers
  • LangGraph
    • Stateful multi-step agent graphs
    • Conditional branching, human-in-the-loop nodes
    • Persistent state & checkpointing across sessions
  • AutoGen & Multi-Agent Systems
    • Collaborative AI agents with role assignments
    • Groupchat, nested chats, tool integration
    • Multi-agent coordination patterns
  • RAG — Retrieval-Augmented Generation
    • Why RAG — LLM hallucination & knowledge limits
    • Document loading, text splitting, embedding models
    • Pinecone, ChromaDB, FAISS — vector store setup
    • Semantic search, hybrid retrieval (dense + sparse)
    • Re-ranking, query expansion, contextual compression
  • Agentic AI Projects
    • Research agent with web search & summarisation
    • Private document Q&A chatbot (RAG-powered)
    • Multi-agent customer support automation system
  • Model Serving APIs
    • FastAPI — building REST APIs for ML models
    • Streamlit — interactive model demo applications
    • Gradio — rapid prototyping for AI demos
  • Containerisation
    • Docker — packaging models & dependencies into containers
    • Kubernetes — scaling ML services in production
  • Experiment Tracking
    • MLflow — logging runs, metrics, parameters, artifacts
    • Model registry, reproducibility, model versioning
  • CI/CD for ML
    • GitHub Actions — automated training & deployment pipelines
    • Testing ML code, data validation, model quality gates
  • Cloud AI Platforms
    • AWS SageMaker — training jobs, hyperparameter tuning, endpoints
    • Google Vertex AI — managed ML pipelines, model garden
    • AWS Bedrock — GenAI APIs at enterprise scale
  • Model Monitoring
    • Data drift & concept drift detection methods
    • Performance dashboards, alerting strategies
    • A/B testing models in production
  • Career Preparation
    • ATS-optimised resume building & formatting
    • LinkedIn profile optimisation — headline, about, skills, projects
    • GitHub portfolio — professional project presentation
  • Interview Mastery
    • 100+ curated DS & AI interview questions with model answers
    • 3+ technical mock interviews with detailed feedback
    • System design for ML — how to answer design questions
  • Soft Skills
    • Quantitative aptitude & logical reasoning practice
    • Group discussion (GD) training & confidence building
    • Communication, stakeholder presentation, data storytelling
  • Capstone Projects (5+)
    • Customer churn prediction — end-to-end ML pipeline
    • Fraud detection system with real-time scoring API
    • LLM-powered private document Q&A chatbot (RAG)
    • Real-time sales forecasting dashboard in Power BI
    • Agentic AI research assistant with LangGraph
Tool Stack
20+ Tools You'll Work With

Every tool is used hands-on in live lab sessions — not just mentioned in a slide.

Python
PostgreSQL
Pandas
NumPy
Scikit-learn
TensorFlow
PyTorch
LangChain
OpenAI API
Hugging Face
Tableau
Power BI
Python
PostgreSQL
Pandas
NumPy
Scikit-learn
TensorFlow
PyTorch
LangChain
OpenAI API
Hugging Face
Tableau
Power BI
LangGraph
Pinecone
ChromaDB
AWS SageMaker
Vertex AI
FastAPI
Docker
Kubernetes
MLflow
Git & GitHub
Jupyter Lab
Ollama
LangGraph
Pinecone
ChromaDB
AWS SageMaker
Vertex AI
FastAPI
Docker
Kubernetes
MLflow
Git & GitHub
Jupyter Lab
Ollama
Career Outcomes
Job Roles After This Course

2026 salary ranges based on Bangalore market data. Our graduates are placed in these exact roles.

🧪
🔥 Hot
Data Scientist
₹10L – ₹28L / yr

Build predictive models, conduct statistical analyses, and drive data-informed business decisions at scale.

⚙️
▲ High Demand
ML Engineer
₹12L – ₹32L / yr

Design, train, and deploy ML systems that power intelligent product features at scale.

🤖
🔥 Hot
AI Engineer
₹14L – ₹35L / yr

Build AI-powered products with LLMs, agents, and neural networks for industry applications.

🔥 #1 in 2026
GenAI Specialist
₹16L – ₹40L / yr

Build GenAI applications, fine-tune LLMs, and design RAG pipelines — the hottest role in 2026.

📊
▲ High Demand
Data Analyst
₹6L – ₹18L / yr

Analyse data, build dashboards in Tableau and Power BI, and deliver actionable business insights.

🚀
↑ Rising
MLOps Engineer
₹14L – ₹30L / yr

Deploy and monitor ML systems — the critical bridge between data science and production engineering.

💬
↑ Rising
NLP / LLM Engineer
₹12L – ₹28L / yr

Build language models, document intelligence pipelines, and conversational AI applications.

☁️
▲ High Demand
Cloud AI Engineer
₹15L – ₹35L / yr

Design and manage AI infrastructure on AWS and GCP — scalable, secure, cost-optimised.

Pricing
Simple, Transparent Fee

One price. Everything included. No hidden costs, no surprises.

Course Fee 2026
₹40,000
Complete 6.5-month program · All 5 modules included
All 5 modules — Core Python to ML/AI/DL/NLP
2026 additions — GenAI, LLMs, Agentic AI, MLOps
5+ live industry capstone projects
Unlimited cloud lab access throughout program
Resume & LinkedIn profile workshop
3+ mock technical interviews with feedback
Placement assistance with 150+ companies
Globally recognised InfoDrafters certificate
Enrol Now — ₹40,000
0% EMI available · Merit-based scholarships for deserving candidates
Duration
6.5 Months
Weekday · Weekend · Online · Hybrid
5 Modules
  • Core Python 23 chapters
  • Advanced Python 5 chapters
  • SQL Basic & Advanced 11 chapters
  • Applied Business Statistics 8 chapters
  • ML · AI · DL · NLP 50+ chapters
Next Batch
July 2026
Limited seats — enrol early to reserve your spot
Reserve a Seat →
Learning Journey
Your 6.5-Month Roadmap

A clear, structured path from enrolment to placement — every step mapped out.

📋
Enrol & Onboard

Book your demo, choose your batch timing, complete enrolment. Access all course materials immediately.

🐍
Foundations

Core Python → Advanced Python → SQL → Statistics. Rock-solid fundamentals over months 1–3.

🤖
ML / AI / DL / NLP

Machine learning, deep learning, NLP, GenAI, and Agentic AI systems in months 3–5.

🗂️
Capstone Projects

Build 5+ production-ready projects. A portfolio that hiring managers shortlist in 30 seconds.

🏆
Placement & Beyond

Resume, mock interviews, GD training, referrals. We work until you're placed and thriving.

Program Outcomes
What You'll Achieve

Tangible, measurable results — not vague promises. Here's exactly what you walk away with.

🏆

Job-Ready in 6.5 Months

Graduate fully prepared for Data Scientist, ML Engineer, AI Engineer, or GenAI Specialist roles at mid-to-senior levels at Bangalore's top tech companies and startups.

🗂️

A Portfolio That Wins Interviews

5+ real-world GitHub projects that demonstrate your capabilities — including an LLM chatbot, a production ML pipeline, and an AI-powered analytics dashboard.

🚀

Ship Real AI Products

Go beyond Jupyter notebooks — deploy actual AI systems including a fine-tuned LLM, a RAG-powered document chatbot, and an Agentic AI assistant running live in production.

🎯

Convert Every Interview

Our mock interview program, 100+ curated DS/AI questions, GD training, and HR preparation ensure you turn every shortlist into an offer letter — not just a rejection email.

🌐

A Powerful Alumni Network

Join 3,500+ InfoDrafters alumni at Amazon, Flipkart, Infosys, TCS, Wipro, KPMG, Deloitte, and hundreds of Bangalore startups. Your batchmates become your referral network for life.

📜

Globally Recognised Certificate

InfoDrafters certification is trusted by 150+ hiring partners. It elevates your LinkedIn, clears ATS filters, and adds hard credibility to every job application you send out.

Student Stories
What Our Alumni Say

Real outcomes. Real people. No exaggeration, no cherry-picking.

★★★★★
"I joined as a mechanical engineer with zero coding experience. Six months later I got placed as a Data Analyst at a fintech company in Koramangala. The course structure is brilliant — moving from Python to ML felt completely natural."
RK
Ravi Kumar
Data Analyst · Koramangala Fintech · Batch 2025
★★★★★
"The GenAI and LLM module alone was worth the entire fee. I landed an AI Engineer role with a ₹22L package. The mock interviews were brutal in a good way — I walked into the real ones completely prepared and confident."
PS
Priya Sharma
AI Engineer · MNC Product Company · Batch 2025
★★★★★
"Coming from an IT background, I wanted to pivot to Data Science. InfoDrafters' trainers are actual practitioners who bring current real-world problems into every class. Got placed within 3 weeks of program completion."
AM
Arjun Menon
Data Scientist · IT Services Firm · Batch 2025
Who Should Enrol
This Program is Built For You

No matter your background, we have a proven path to get you placed in Data Science & AI.

🎓
Fresh Graduates

CS, IT, or any engineering graduates — launch directly into Data Science & AI with a structured program that assumes nothing.

💻
Software Developers

Transition from backend/frontend to ML/AI Engineering. Your coding experience will accelerate your learning dramatically.

📊
Data Analysts

Level up from Excel and Tableau to Machine Learning, Generative AI, and AI-powered analytics dashboards.

🖥️
IT Professionals

Sysadmins and IT ops — step into cloud AI, data engineering, and MLOps to dramatically increase your market value.

🔄
Career Changers

Finance, marketing, or non-tech backgrounds — our foundational modules ensure everyone catches up quickly and thrives.

👔
Business Professionals

Managers and entrepreneurs who want to leverage AI, analytics, and LLMs to make smarter data-driven business decisions.

FAQ

Got Questions?
We Have Answers.

Everything you need to know before enrolling. Still unsure? Our counsellors are available Mon–Sat, 9 AM – 8 PM.

Talk to a Counsellor
Call Us Directly
+91 89047 40434

Mon – Sat  ·  9 AM – 8 PM

The complete fee is ₹40,000 for the full 6.5-month program. This covers all 5 modules, GenAI & LLM 2026 additions, 5+ live projects, placement support, and your globally recognised certificate. 0% EMI options are available. Contact +91 8904740434 for scholarship eligibility.
The program is 6.5 months of intensive training. We offer weekday batches (Mon–Fri), weekend batches (Sat–Sun), evening batches for working professionals, and fully online live classes. New batches start every month — contact us for the upcoming schedule.
No prior coding experience is needed. Module 1 (Core Python) starts from absolute basics. We also offer a free 1-week foundation bootcamp for complete beginners. For statistics, we cover all required math from scratch — basic arithmetic is sufficient to start.
The 5 modules are: (1) Core Python — 23 chapters covering all fundamentals and OOP; (2) Advanced Python — NumPy, Pandas, Matplotlib, Seaborn; (3) SQL Basic & Advanced — PostgreSQL, window functions, CTEs, BI projects; (4) Applied Business Statistics — probability, distributions, hypothesis testing; (5) ML · AI · Deep Learning · NLP — including 2026 additions of GenAI, LLMs, Agentic AI, and MLOps. Full syllabus is expandable in the Syllabus section above.
End-to-end placement support: ATS-optimised resume building, LinkedIn profile overhaul, GitHub portfolio review, 100+ curated DS/AI interview Q&As, 3+ technical mock interviews with detailed feedback, HR round prep, GD training, and direct hiring connections with 150+ partner companies. Our 2025 batch achieved a 98% placement rate.
Based on our 2025 placement data: freshers typically land ₹6L–₹12L packages. Professionals with 2–3 years of prior experience who upskill through our program often secure ₹15L–₹30L. GenAI Specialists — the hottest role in 2026 — are commanding ₹20L–₹40L packages in Bangalore.
2026 additions: Generative AI (GPT-4o, Claude 3.5, Gemini 1.5), Large Language Models with LoRA/QLoRA fine-tuning, Prompt Engineering, Agentic AI with LangChain + LangGraph + AutoGen, RAG systems with Pinecone/ChromaDB, MLOps with Docker/Kubernetes/MLflow, AWS Bedrock, Google Vertex AI, Power BI Copilot, and AI Ethics & Responsible AI.
Both are fully available. In-person at our BTM Layout campus gives you access to modern labs and face-to-face mentoring. Online live classes deliver the same curriculum with real-time interaction. A hybrid option is also offered. All sessions are recorded and accessible for revision at any time.
Google Ad — 728×90 Bottom Leaderboard
New Batch · July 2026

Start Your AI Career
This July

New batches start every month. Limited seats per batch — early enrolment gets priority access. Book your free demo class today — no cost, no obligation.

NO. 32, 17th Main Rd, BTM Layout 1st Phase, Bengaluru 560029 +91 8904740434  /  080 41696789 infodrafters05@gmail.com