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Master AI & ML in 12 Weeks in Bangalore — Career Ready Program
A 12-week, live instructor-led programme covering the full applied AI/ML lifecycle — Python, data, classic ML, computer vision, NLP, LLM fine-tuning, agents and MLOps — ending in a deployed capstone system with CI/CD.
Faculty
Curriculum
Format
Outcome
Duration
Batch
US Data Eng. Salary Range*
*Market-wide salary range for the data-engineering-adjacent roles this programme targets, Robert Half 2026 Salary Guide — an industry benchmark, not a claim about this programme…s graduate outcomes.
Why Now — Broad AI/ML Skill Still Clears More Doors Than Narrow GenAI Alone
The 2026 hiring data, sourced — including why ML Engineer remains the largest single AI job category by volume.
Share of all AI/ML job titles held by “ML Engineer” specifically — still the largest single category by volume. SRC: Ravio data, via HeroHunt 2026
Open AI/ML roles globally — postings up 163% (2026→25), a further 74% YoY in 2026. SRC: Dice / LinkedIn / Axial Search, 2026
Projected size of the MLOps market by 2034 — the exact discipline this programme…s final module covers. SRC: KORE1, via HeroHunt 2026
Projected AI engineer role growth 2026–2033, vs. 4% average across all US occupations. SRC: US Bureau of Labor Statistics
CAGR of the global machine learning market, 2026–2030 ($55.8B → $282.13B). SRC: Grand View Research, via Phaidon Intl. 2026
Share of ML engineer listings that prefer domain-specific depth over generalist breadth. SRC: 2026 job-posting analysis, via TekRecruiter
Reality Check — Read Before You Enrol Anywhere
- The oversupply in ML hiring sits at entry and mid level — only around 6% of postings in one large sample are entry-level, and employers increasingly want candidates who have shipped models into production, not just built them in notebooks. That is why this programme ends in a deployed system with CI/CD, not a Jupyter notebook
- 57.7% of ML engineer listings prefer domain depth over generalist versatility. This programme builds broad coverage across the lifecycle — pair it with your own specialisation (healthcare, finance, retail) rather than treating the breadth alone as the differentiator
- A 2026 US market snapshot found roughly 490 new ML engineering postings per week, with 70% at mid or senior level — meaning this programme gets you conversant, not automatically senior. Expect to build additional project depth afterwards to compete at that tier
Skills You Will Master
Hands-on skills, not slide-deck theory. Every skill below maps to a project you actually build during the programme.
Course Curriculum
6 structured modules covering the full applied AI/ML lifecycle, ending in one deployed capstone. 12 weeks total, live cohort pace at 6 hrs/week.
01 Python & OOP Foundations · Weeks 1–2
- Python as the industry standard for AI, ML and backend work
- Rapid prototyping — building and testing real-world ideas quickly
- OOP principles for scalable, maintainable code design
- Interview-focused Python and OOP practice
02 Data Mastery — Clean, Analyse, Unlock Insights · Weeks 3–4
- Data cleaning and preprocessing
- Statistics for turning raw data into decisions
- Visual storytelling — charts and EDA for stakeholders
03 Machine Learning — Turning Data into Business Value · Weeks 5–6
- Predictive analytics — building ML models for real business problems
- Industry-standard algorithms: regression, classification, XGBoost
- Business impact — decision-making, targeting, revenue
04 Vision & Language — the Superpowers of AI · Weeks 7–8
- Computer vision for healthcare, retail and autonomous vehicles
- NLP excellence — from chatbots to document intelligence
- Applied AI patterns used across current industry systems
05 LLM Fine-Tuning & Agents · Weeks 9–10
- Fine-tuning large models (ChatGPT/LLaMA-class) for specialised tasks
- Prompt engineering to control and optimise LLM behaviour
- Building AI agents — multi-step tools that reason, retrieve and act
06 MLOps — Bring Models to Life in the Real World · Weeks 11–12
- Deployment with FastAPI, Streamlit and Docker
- Experiment tracking and monitoring with MLflow
- CI/CD with GitHub Actions for production scaling
Tools & Technologies
Projects — Six Builds Across the AI/ML Lifecycle
- EDA & Insight Report — beginner: clean a messy real-world dataset, run exploratory analysis and produce a stakeholder-ready visual report
- Predictive Business Model — beginner: build and evaluate regression, classification and XGBoost models against a real business outcome such as churn or pricing
- Computer Vision Classifier — intermediate: CNN-based classification on healthcare or retail imagery; preprocessing, transfer learning and evaluation on real image data
- NLP Document Intelligence Tool — intermediate: a chatbot or document-understanding tool built with embeddings and transformer models
- Fine-Tuned Task-Specific LLM — advanced: fine-tune a ChatGPT/LLaMA-class model on a narrow task and evaluate it against the base model
- Capstone: Deployed ML System with CI/CD — advanced: package a model behind FastAPI/Streamlit, add MLflow experiment tracking and wire a GitHub Actions pipeline for automated redeploy
Career Roles You Can Target
Data Scientist
ML Engineer
AI Engineer
CV / NLP Specialist
MLOps Engineer
Certification — Stated Plainly
- This programme does not lead to a proprietary certification exam — there is no single industry-standard certifying body for “ML Engineer” or “Data Scientist”, so we are not inventing a badge
- You receive a SEHVI programme completion certificate after all six modules and the capstone — an in-house record of completion, not third-party accreditation
- What you actually leave with: a deployed capstone with CI/CD plus a GitHub portfolio of shipped projects. With 70% of ML engineering postings at mid or senior level and prioritising production experience, a working deployment pipeline demonstrates more than a certificate of attendance
- Optional and pursued separately: AWS Certified Machine Learning – Specialty, Microsoft Azure AI Engineer Associate — neither is issued by or included in this programme
Who This Is Actually For
- Good fit: Python-literate career switchers who want the full AI/ML lifecycle, not just GenAI
- Good fit: you are targeting Data Scientist, ML Engineer or MLOps titles
- Good fit: you want breadth first and plan to specialise afterwards
- Think twice: zero programming background — Module 01 assumes you can pick up Python fast, not start from zero
- Think twice: you only care about GenAI and agents — the 8-week GenAI Engineering track goes deeper faster on that alone
- Think twice: you have under 6 hrs/week available — this is live-paced, not self-serve
Frequently Asked Questions
Can this really be covered in 12 weeks?
At 6 hrs/week live that is roughly 72 contact hours plus project time outside sessions, across six lifecycle stages. Enough to build working competence and a deployed capstone — not enough to make you senior-level in all six areas simultaneously. Expect conversant breadth, then pick one area to go deeper in afterwards.
Do I need coding experience?
Yes. Module 01 assumes you can move fast through Python and OOP fundamentals rather than starting from zero. If you are new to programming, budget separate ramp-up time before week 1 — the optional Python Fast-Track module is the on-ramp.
Is broad AI/ML training still worth it versus a narrow GenAI course?
ML Engineer remains 45% of all AI/ML job titles by volume — the largest single category — so broad lifecycle skill still clears the most doors by raw count. The tradeoff: 57.7% of listings still prefer domain depth over generalist breadth, so treat this programme as a foundation to specialise from, not a substitute for specialising.
Is there placement support?
This page makes no placement-rate or guarantee claim, because we do not have verified data for this programme to back one. Our counsellors will tell you exactly what support is available for your cohort.
What are the prerequisites?
Working Python (functions, classes, basic OOP) and basic comfort with statistics. Module 01 moves fast through Python fundamentals rather than teaching them from zero — use the optional Python Fast-Track module first if you need that on-ramp.
What training modes are offered?
Live online, cohort-based, 6 hrs/week for 12 weeks. Sessions are recorded for review; confirm the current recording-access policy with your programme coordinator, since it can vary by cohort.
What if I miss a live session?
Recorded sessions are the fallback, but this programme is project-paced and each of the six modules builds on the last. Missing a live lab means catching up independently before the next module starts — plan for that if your schedule is unpredictable.
Get Course Details
Our counsellor will contact you within 30 mins with the syllabus, batch dates & fees.
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