Artificial intelligence is the new thing turning the industries and skilled AI workers are becoming highly demanded. IFDA offers industry-focused AI engineering courses that prepare students for high-demand careers. It includes machine learning, natural language processing, computer vision, robots and AI ethics in an academic program that combines theory with applied training. Whether you attend our offline AI engineering courses in Delhi or enroll in an online AI engineering course near me, you’ll gain real-world skills that employers value. IFDA Institute is recognized for offering top AI courses in Delhi and is a leading AI institute in Delhi. By joining an AI course in Delhi with IFDA, students can gain practical knowledge and career-ready skills in the fast-growing field of Artificial Intelligence
Course Highlights
1. |
Zero-to-production pathway |
2. |
Latest, industry-relevant stack and practices with real projects and a capstone. |
3. |
Efficient fine-tuning (PEFT/LoRA/QLoRA) and quantization for cost-effective deployment. |
4. |
High-throughput model serving (ONNX Runtime, NVIDIA Triton, vLLM) with simple APIs. |
5. |
Governance-ready workflows: documentation, basic privacy controls, and safety guardrails. |
6. |
Quality-by-design: experiment tracking, evaluation, drift monitoring, data-quality checks, and explainability. |
7. |
5 Assignments |
8. |
240 Hours Of Training |
9. |
1 Year Free Backup Classes |
Learning Outcome
| • | Learn the concepts and practice of AI |
| • | Create and implement machine learning models |
| • | NLP, image recognition, and predictive analytics work | Read More |
| • | Learn to use master tools, such as Python, TensorFlow and PyTorch |
| • | Get hands-on-experience in projects |
| • | Prepare to work in the field of AI engineering, data science and automation | Read Less |
Software that you will learn in this course
Course Content
| 1. Computer & Tooling Setup |
| • CLI basics; VS Code; Git/GitHub; Python installs; notebooks (Colab/Jupyter). |
| • Lab: Create a repo; set up virtual env; first notebook. |
| • Deliverable: Hello, Data repo with README. |
| 2.Python Foundations + NumPy/Pandas Primer |
| • Python syntax, functions, modules; arrays, DataFrames, I/O. |
| • Lab: Clean a small CSV; basic summaries. |
| • Deliverable: Data-wrangling notebook. |
| 1. EDA & Visualization |
| • Plotting (line/bar/hist), outliers, correlations; story-driven EDA. |
| • Lab: Mini EDA report. |
| • Deliverable: Hello, Data repo with README. |
| 2. Math You Will Use |
| • Descriptive stats; probability intuition; gradients & loss. |
| • Lab: Compute metrics manually; gradient step demo. |
| • Deliverable: Math-lite worksheet. |
| 3. Data Prep & Validation |
| • Splits (train/val/test), leakage, scaling/encoding, cross-validation. |
| • Lab: Build a preprocessing pipeline. |
| • Deliverable: Reusable sklearn Pipeline. |
| 4. Supervised I (Linear & Logistic) |
| • Bias-variance; regression vs classification; metrics (R2, AUC, F1). |
| • Lab: House price regression. |
| • Deliverable: Model comparison table. |
| 5. Supervised II (Trees & Ensembles) |
| • Decision trees, Random Forests, Gradient Boosting; feature importance. |
| • Lab: Churn classifier. |
| • Deliverable: Tuned model + report. |
| 6. Unsupervised + Experiment Tracking |
| • K-Means, PCA; when/why unsupervised; intro to MLflow. |
| • Lab: PCA visualization + MLflow run logging. |
| • Deliverable: Logged experiments with tags. |
| 1. Prompting & Structured Outputs |
| • Prompt patterns; JSON outputs; tool/function calling. |
| • Lab: Structured answerer with validation. |
| • Deliverable: Prompt kit. |
| 2. Embeddings & Vector Stores |
| • Chunking, metadata, similarity/hybrid search, filters. |
| • Lab: Index a PDF corpus. |
| • Deliverable: Vector index + queries. |
| 3. RAG Architectures |
| • Retriever-generator loop; context windows; grounding & citations. |
| • Lab: Basic RAG bot. |
| • Deliverable: Working QA over docs. |
| 4. RAG Evaluation & Reranking |
| • Precision/recall for retrieval; rerankers; hallucination checks. |
| • Lab: Build an eval set; add reranker. |
| • Deliverable: Eval report + charts. |
| 5. Agents with LangGraph |
| • Agent state, tools, planning, retries/fallbacks. |
| • Lab: Tool-using agent (search/files/calculator). |
| • Deliverable: Agent graph diagram + code. |
| 6. Efficient Fine-tuning (PEFT/QLoRA & Quantization) |
| • LoRA adapters; 4-bit quant; when to fine-tune vs retrieve. |
| • Lab: QLoRA fine-tune of a small open LLM. |
| • Deliverable: Adapter weights + eval. |
| 7. Shipping an LLM App |
| • UX prompts, rate limits, cost control, logging. |
| • Lab: Wrap your RAG/agent into a minimal app. |
| • Deliverable: Demoable prototype. |
| 1. PyTorch Fundamentals |
| • Tensors, autograd, modules, optimizers. |
| • Lab: Train a tiny MLP. |
| • Deliverable: Training loop template. |
| 2. Training Practice & Regularization |
| • Over/underfitting; dropout, weight decay; LR schedules; early stopping. |
| • Lab: Overfit-then-fix exercise. |
| • Deliverable: Reproducible training script. |
| 3. CNNs for Vision |
| • Convolutions, pooling, augmentation; metrics. |
| • Lab: Fashion-MNIST classifier. |
| • Deliverable: Saved model + eval. |
| 4. Transfer Learning (Vision) |
| • Freeze/unfreeze; fine-tuning; checkpointing. |
| • Lab: Pretrained CNN on custom images. |
| • Deliverable: Best model + README. |
| 5. Transformers & Tokenization |
| • Tokenizers, embeddings, attention; encoder vs decoder. |
| • Lab: Sentiment classifier with a small Transformer. |
| • Deliverable: Training/eval notebook. |
| 6. NLP Fine-tuning & NER |
| • Task heads, batching, padding, masking; eval best practices. |
| • Lab: NER or multi-class text classification. |
| • Deliverable: Metrics dashboard. |
| 7. Debugging, Checkpoints & Mixed Precision |
| • Profiling, AMP, reproducibility, seeds; error handling. |
| • Lab: Speed-vs-accuracy experiment. |
| • Deliverable: Repro checklist + results. |
| 1. Packaging & APIs |
| • FastAPI design, dependency management, Docker basics, CI/CD. |
| • Lab: Serve a sklearn/PyTorch model via API. |
| • Deliverable: Dockerized service. |
| 2. Optimized Inference (ONNX Runtime) |
| • Export/convert; latency vs throughput; CPU/GPU trade-offs. |
| • Lab: Benchmark ONNX vs framework runtime. |
| • Deliverable: Bench sheet + charts. |
| 3. Serving LLMs at Scale (Triton & vLLM) |
| • Model repositories, batching, dynamic shapes; high-throughput LLM serving. |
| • Lab: Stand up vLLM/Triton and hit with a load test. |
| • Deliverable: Load test report. |
| 4. Monitoring, Drift & Data Quality |
| • Metrics, traces, MLflow registry; drift (data/concept); Great Expectations/Evidently; A/B tests. |
| • Lab: Add checks & dashboards to your API. |
| • Deliverable: Monitoring playbook. |
| 1. Governance, Privacy & Safety Guardrails |
| • Risk thinking; privacy basics; input/output filtering; incident runbooks. |
| • Lab: Add guardrails to your LLM app. |
| • Deliverable: Safety checklist. |
| 2. Explainability & Documentation |
| • SHAP/LIME; model cards; communicating limits. |
| • Lab: Explain a prediction and write a model card. |
| • Deliverable: Explainability report. |
| 1. Scope & Data Ingestion |
| • Problem framing, KPIs, data contracts, architecture sketch. |
| • Deliverable: Proposal + plan. |
| 2. MVP Build |
| • First working vertical slice; iterate quickly. |
| • Deliverable: MVP demo. |
| 3. Performance & Quality Hardening |
| • Optimize latency/cost; add evals/monitoring; fix failure modes. |
| • Deliverable: Perf & quality report. |
| 4. Deploy, Document & Showcase |
| • Final deployment; README, model card, demo video; presentation.. |
| • Deliverable: Live demo + repo. |
Jobs and Career Opportunity After Completing Course
After completing this course you will get many job and career opportunities easily in the computer and IT field like e-commerce, government organizations, and security companies. You can start your early earnings with this course because there is no education criteria for this course and every business needs that kind of skilled employee. IFDA Institute is known for offering the best AI courses in Delhi. As a leading AI institute in Delhi, it provides practical learning, and enrolling in an AI course in Delhi will help you gain job-ready skills for a successful career.
| Job profile After completing this course |
Average salary ( 1+ year experience) |
|---|---|
| AI/ML Engineer | ₹3.6 L |
| Machine Learning Engineer | ₹10–12 |
| Data Scientist / Applied Scientist | ₹12–15 L |
| NLP / LLM Engineer | ₹10–20 L |
| MLOps / Model Deployment Engineer | ₹10–15 L |
| AI Product Engineer | ₹8–15 L |
| Data Analyst (Python/SQL) | ₹4–8 L (entry-level) |
Backup Class
Flexible Timing
Fees Installment
Expert Trainer
100% job assistance
Free Library
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Live Project
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Practical learning
I recently joined the AI Engineering course at IFDA Institute, Kalkaji, and I’m really happy with my decision. The institute balances theory and practical sessions very well, and the smart classes make it easy to understand complex concepts. The course is worth the fees, and I feel it’s one of the best options for anyone looking for AI Engineering courses in Delhi.
My experience at IFDA Institute has been excellent. The faculty is supportive, and the modules are explained in a practical way. I was searching for an AI Engineering course near me, and I’m glad I chose IFDA. The AI Engineering course fees are affordable compared to the quality of training, and the smart classrooms make learning more engaging.
IFDA Institute is a great choice for students interested in AI Engineering courses. The syllabus is well-structured, covering both theoretical knowledge and practical applications. The trainers are very helpful, and the fees are reasonable for the value provided. For anyone planning to start their career in AI, this institute offers one of the most reliable AI Engineering courses in Delhi.
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Frequently Asked Questions
IFDA Institute offers one of the leading AI Engineering courses in Delhi with a strong focus on practical learning. Students get access to expert faculty, smart classrooms, and real-world projects. The course is structured to cover Python, machine learning, and AI concepts, helping students gain the knowledge and confidence to build a successful career.
The AI Engineering course fees at IFDA Institute are affordable compared to the quality of training provided. The institute ensures value for money by offering a well-structured syllabus, practical exposure, and smart class learning. Students not only understand the concepts but also learn industry-level skills, making the fee investment highly worthwhile for long-term career growth.
IFDA Institute provides industry-focused AI Engineering courses that blend theoretical knowledge with practical applications. The experienced faculty ensures each student learns through real-world projects and hands-on training. With smart classroom facilities and supportive mentors, students gain a strong foundation in artificial intelligence, making IFDA one of the best institutes for future-ready AI professionals.
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