LogicMojo — AI & Machine Learning Course
Specialist AI provider, live IST cohorts
LogicMojo is a specialist rather than a marketplace: one flagship AI and machine learning track, taught live to Indian working professionals, with the curriculum revised as the stack moves. What it is really selling is capability density — the shortest credible path from working engineer to someone who can architect, fine-tune, deploy and monitor an AI system. Disclosure repeated: this page is published by LogicMojo, and it is scored on the same rubric as the other nine.
Curriculum & tools
Python, data and SQL foundations; maths intuition and classical ML with evaluation rigour; deep learning and transformers in PyTorch; GenAI and LLMs including open-weight models and local inference; embeddings, vector databases and production RAG; fine-tuning with SFT and LoRA/QLoRA plus agents, LangGraph, CrewAI, AutoGen and MCP; MLOps and LLMOps with MLflow, FastAPI, Docker, CI/CD and monitoring. The only program on this list rated Deep or Comprehensive across every premium-pay row of Table 2 — fine-tuning, agents, frameworks, MCP, MLOps and deployment.
Fees, duration & EMI
Fee ₹87,000, GST inclusive. Duration 7 months (about 30 weeks). EMI available; confirm whether no-cost EMI is running this month and the exact refund cut-off date in writing. Check the current fee on logicmojo.com ↗
Prerequisites & flexibility
Basic coding helps but a Python and maths bridge is provided; live weekend batch, Saturday and Sunday 9:00 AM–12:00 PM IST, with the next cohort listed as starting the coming month; 10–15 hours a week including self-study; batch deferral available.
Projects & mentorship
10–15 progressive builds culminating in a learner-designed capstone that must be deployed, not merely notebooked. Human code review throughout, everything documented for GitHub.
Placement & job assistance
Career guidance, portfolio review, AI-role-specific interview preparation and project-defence practice. No bond, no ISA. It is explicitly not a guaranteed-placement program.ASCIDept. of Consumer Affairs
Career outcomes & salary potential
Credibly prepares for AI Engineer, GenAI/LLM Engineer, ML Engineer and AI Agent Developer roles; indicative bands for those roles [VERIFY]. Those bands belong to the roles and the market, not to the course.AmbitionBox: AI EngineerAmbitionBox: ML EngineerLevels.fyi (India, ML/AI)Payscale: ML Engineer
ROI verdict
Mid-band fee against the highest capability ceiling here gives the strongest payback framing on this list — but only if you finish. Ten to fifteen hours a week for months is the real price.
Why this course for a high-paying AI career
The deepest end-to-end 2026 AI stack on this list — classical ML through fine-tuning, agents and LLMOps — delivered live in IST cohorts with human code review, which is the combination that lets a working engineer interview credibly for AI Engineer and GenAI Engineer roles rather than analyst roles.
Salary potential & role outcomes
- AI/ML Engineer (0–2 yrs relevant) — ₹6–14 LPAEntry band, metro product + services mix [verify current]
- AI Engineer / GenAI Engineer (3–6 yrs) — ₹18–35 LPAProduction RAG + evaluation ownership [verify current]
- LLM / Agent Engineer (senior) — ₹30–55 LPAThin supply; product companies and AI-native startups [verify current]
- MLOps / Platform Engineer — ₹16–32 LPADeployment, monitoring, cost control [verify current]
Prerequisites & who it suits
Any engineering or quantitative background; non-CS graduates are onboarded through a Python + SQL + maths-intuition bridge before the ML block. No prior ML required. Comfortable with 10–15 hours a week for 7 months (about 30 weeks) is the real prerequisite.
Teaching methodology
Step-by-step and cumulative: concept → live implementation → guided lab → graded build → review. Each module ends with a build that becomes a portfolio artefact, so the curriculum and the portfolio are the same object rather than two parallel workstreams.
| AI curriculum area | What is covered | Depth |
|---|---|---|
| Python & software foundations | Python for data work, OOP, testing basics, Git/GitHub workflow, environment and dependency management. | Deep |
| SQL & data engineering basics | Joins, window functions, query tuning, working with warehouses and Pandas/Polars pipelines. | Good |
| Statistics & maths intuition | Distributions, hypothesis testing, linear algebra and calculus intuition tied directly to model behaviour rather than exam-style proofs. | Good |
| Machine Learning | Regression, trees, boosting (XGBoost/LightGBM), clustering, feature engineering, leakage, cross-validation and metric selection — with evaluation rigour, the part interviews actually probe. | Deep |
| Deep Learning | PyTorch, CNNs, RNNs, attention and transformer internals implemented rather than described. | Deep |
| NLP | Tokenisation, embeddings, sequence models, transfer learning with Hugging Face, evaluation of text systems. | Deep |
| Computer Vision | CNN architectures, transfer learning, detection/segmentation basics, multimodal touchpoints. | Good |
| Generative AI & LLMs | Model families incl. open-weight models, local inference, context windows, structured outputs, cost and latency engineering. | Deep |
| Prompt engineering | System design of prompts, few-shot, chain-of-thought patterns, guardrails — taught as a component, not the product. | Deep |
| RAG & vector databases | Chunking strategy, embedding choice, hybrid search, re-ranking, FAISS/Chroma/Pinecone/pgvector, and RAG evaluation (faithfulness, context precision). | Deep |
| LangChain / LangGraph & agents | Tool calling, state machines, multi-agent orchestration with LangGraph, CrewAI and AutoGen, plus MCP-style tool interfaces. | Deep |
| Fine-tuning | SFT, LoRA/QLoRA, dataset curation, evaluation harnesses, and when fine-tuning is the wrong answer versus retrieval. | Deep |
| MLOps / LLMOps | MLflow experiment tracking, FastAPI services, Docker, CI/CD, drift and cost monitoring, observability for LLM apps. | Deep |
| Cloud & deployment | Containerised deployment, GPU/inference cost trade-offs, serving patterns on major cloud providers. | Good |
Projects & industry readiness
- Progressive builds: 10–15 graded builds, each one a component an interviewer can interrogate: an ML pipeline with honest validation, a transformer from scratch, a production RAG service with evaluation, a fine-tuned domain model, an agentic workflow with tools.
- Capstone: Learner-designed capstone that must be deployed with a public endpoint, monitoring and a written design doc — notebooks are not accepted as a finished capstone.
- Datasets: Real, messy datasets and domain corpora rather than curated toy sets; the failure modes are the teaching material.
- Portfolio output: Every project documented for GitHub with README, architecture diagram, evaluation results and cost notes.
Tooling matches what 2026 job descriptions list: PyTorch, Hugging Face, LangChain/LangGraph, vector stores, MLflow, FastAPI, Docker and CI/CD. The measurable readiness signal is that graduates can answer 'why this chunk size', 'how did you evaluate it' and 'what did it cost per 1,000 requests' — the three questions that separate an AI engineer offer from an analyst offer.
Learning support, doubt clearing & mentorship
- Doubt clearing: In-session resolution plus a between-sessions mentor channel; recorded sessions with a structured catch-up path for missed weeks.
- Peer group: Fixed cohort with weekly accountability; a working-professional peer set rather than an anonymous forum.
- Teaching assistants: TA support for debugging and code review escalation alongside the instructor.
- Flexibility: Batch deferral available; live weekend batch on Saturday and Sunday, 9:00 AM–12:00 PM IST, designed around Indian work hours.
- Mentorship: Live instructor-led classes with direct instructor access, mentor channels between sessions and human code review on submissions. Portfolio and project-defence reviews are one-to-one.
Placement & job-assistance details (read the contract, not the banner)
- Model: Job assistance and career guidance — explicitly NOT a placement guarantee, no bond, no ISA. We state this plainly because we publish this page.
- Interview preparation: AI-role-specific mock interviews: ML fundamentals, system design for AI, project-defence drills where your own capstone is attacked.
- Resume & LinkedIn: Resume rewrite workshops that convert projects into outcome bullets, plus LinkedIn headline/About/featured-project optimisation for AI recruiter search terms.
- Career counselling: Role-targeting sessions (AI Engineer vs Data Scientist vs MLOps), band expectations by city and company type, and negotiation framing.
- Hiring partners: Alumni report offers across Indian product companies, GCCs and services firms; company names and outcome details are published as learner-submitted stories at logicmojo.com/success-story rather than as an aggregate placement percentage. No placement percentage is claimed here because we will not publish a denominator we cannot show.
- Post-course support: Career support continues past the final module while you are actively interviewing [verify current duration with the counsellor in writing].
Learner feedback & reported transitions
Java backend developer, 4 yrs, services firm, Pune → AI Engineer
Indian product companyhigh-teens ₹ LPA band
Switched on the strength of a deployed RAG service with an evaluation dashboard; source: learner story at logicmojo.com/success-story [verify current]
Data analyst, 3 yrs, BFSI, Bengaluru → Machine Learning Engineer
GCCmid-teens ₹ LPA band
Internal move after the MLOps module; learner-submitted story [verify current]
Non-CS graduate, self-taught Python → GenAI Engineer (junior)
AI-native startupentry AI band
Hired off an agentic workflow project defended in a live round; learner-submitted story [verify current]
Strengths
- Deepest coverage of the exact skills where 2026 pay premiums sit
- Genuinely live IST instruction with in-session doubt resolution
- Human code review rather than auto-graded notebooks
- Mandatory deployed capstone — deployment is the hired-vs-not line
- No bond or ISA, and no salary claims attached to the program
Limitations
- Not the cheapest — PW Skills, GUVI and free tracks exist
- No university credential for HR filters or promotion cases
- Not a large placement machine; DeepLearning AI is the honest pick if that is the purchase
- Live format punishes unpredictable schedules
- Smaller brand recognition than the ₹2L+ platforms
Best-fit learner
- Working engineers targeting AI-engineer role bands
- IT-services engineers wanting production depth, not a certificate
- ROI buyers weighing capability per rupee
Avoid if
- You need a university tag to clear an internal promotion panel
- Your schedule cannot support live sessions
- You want a research or PhD pathway
Rating block
Overall 9.1/10 · Capability ceiling Level 4–5
How to re-verify this LogicMojo reviewLogicMojo AI courseLogicMojo GenAI courseLogicMojo learner storiesAmbitionBox: AI EngineerNaukri: ML jobsASCIReserve Bank of IndiaLinks open on the publisher's own site. Last checked 25 Aug 2026.
Explore the LogicMojo AI course curriculum and live batches →Open the official LogicMojo page ↗Enroll Now
