AI Engineering is a job-oriented, project-heavy program for absolute beginners. We start from computer and Python basics and progress through Classical Machine Learning, Deep Learning with PyTorch, and modern Large Language Model (LLM) systems including Retrieval-Augmented Generation (RAG) and tool-using agents. The program finishes with real deployment, monitoring, and practices so you can ship production-grade AI applications with confidence.
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
| • | Use Python and core data libraries to prepare datasets, visualize patterns, and write clean, reusable code. |
| • | Build, evaluate, and compare classical ML models; choose metrics and validation strategies. |
| • | Train deep neural networks; fine-tune Transformer models for NLP tasks. | Read More |
| • | Serve models via APIs; containerize deployments; optimize cost and performance. |
| • | Track experiments and versions; run A/B tests; set up alerts and dashboards for model health. |
| • | Apply essential Responsible AI practices: model cards, guardrails, privacy basics, and explainability. | Read Less |
Software that you will learn in this course
Course Content
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. |
Python Foundations + NumPy/Pandas Primer | |
| • | Python syntax, functions, modules; arrays, DataFrames, I/O. |
| • | Lab: Clean a small CSV; basic summaries. |
| • | Deliverable: Data-wrangling notebook. |
EDA & Visualization | |
| • | Plotting (line/bar/hist), outliers, correlations; story-driven EDA. |
| • | Lab: Mini EDA report. |
| • | Deliverable: Hello, Data repo with README. |
Math You Will Use | |
| • | Descriptive stats; probability intuition; gradients & loss. |
| • | Lab: Compute metrics manually; gradient step demo. |
| • | Deliverable: Math-lite worksheet. |
Data Prep & Validation | |
| • | Splits (train/val/test), leakage, scaling/encoding, cross-validation. |
| • | Lab: Build a preprocessing pipeline. |
| • | Deliverable: Reusable sklearn Pipeline. |
Supervised I (Linear & Logistic) | |
| • | Bias-variance; regression vs classification; metrics (R2, AUC, F1). |
| • | Lab: House price regression. |
| • | Deliverable: Model comparison table. |
Supervised II (Trees & Ensembles) | |
| • | Decision trees, Random Forests, Gradient Boosting; feature importance. |
| • | Lab: Churn classifier. |
| • | Deliverable: Tuned model + report. |
Unsupervised + Experiment Tracking | |
| • | K-Means, PCA; when/why unsupervised; intro to MLflow. |
| • | Lab: PCA visualization + MLflow run logging. |
| • | Deliverable: Logged experiments with tags. |
PyTorch Fundamentals | |
| • | Tensors, autograd, modules, optimizers. |
| • | Lab: Train a tiny MLP. |
| • | Deliverable: Training loop template. |
Training Practice & Regularization | |
| • | Over/underfitting; dropout, weight decay; LR schedules; early stopping. |
| • | Lab: Overfit-then-fix exercise. |
| • | Deliverable: Reproducible training script. |
CNNs for Vision | |
| • | Convolutions, pooling, augmentation; metrics. |
| • | Lab: Fashion-MNIST classifier. |
| • | Deliverable: Saved model + eval. |
Transfer Learning (Vision) | |
| • | Freeze/unfreeze; fine-tuning; checkpointing. |
| • | Lab: Pretrained CNN on custom images. |
| • | Deliverable: Best model + README. |
Transformers & Tokenization | |
| • | Tokenizers, embeddings, attention; encoder vs decoder. |
| • | Lab: Sentiment classifier with a small Transformer. |
| • | Deliverable: Training/eval notebook. |
NLP Fine-tuning & NER | |
| • | Task heads, batching, padding, masking; eval best practices. |
| • | Lab: NER or multi-class text classification. |
| • | Deliverable: Metrics dashboard. |
Debugging, Checkpoints & Mixed Precision | |
| • | Profiling, AMP, reproducibility, seeds; error handling. |
| • | Lab: Speed-vs-accuracy experiment. |
| • | Deliverable: Repro checklist + results. |
Packaging & APIs | |
| • | FastAPI design, dependency management, Docker basics, CI/CD. |
| • | Lab: Serve a sklearn/PyTorch model via API. |
| • | Deliverable: Dockerized service. |
Optimized Inference (ONNX Runtime) | |
| • | Export/convert; latency vs throughput; CPU/GPU trade-offs. |
| • | Lab: Benchmark ONNX vs framework runtime. |
| • | Deliverable: Bench sheet + charts. |
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. |
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. |
Governance, Privacy & Safety Guardrails | |
| • | Risk thinking; privacy basics; input/output filtering; incident runbooks. |
| • | Lab: Add guardrails to your LLM app. |
| • | Deliverable: Safety checklist. |
Explainability & Documentation | |
| • | SHAP/LIME; model cards; communicating limits. |
| • | Lab: Explain a prediction and write a model card. |
| • | Deliverable: Explainability report. |
Scope & Data Ingestion | |
| • | Problem framing, KPIs, data contracts, architecture sketch. |
| • | Deliverable: Proposal + plan. |
MVP Build | |
| • | First working vertical slice; iterate quickly. |
| • | Deliverable: MVP demo. |
Performance & Quality Hardening | |
| • | Optimize latency/cost; add evals/monitoring; fix failure modes. |
| • | Deliverable: Perf & quality report. |
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. This course will also allow you to do work from home. After learning this course you can set up your own business.
| 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
.webp)
Live Project
.webp)
Practical learning
I am a student of IFDA Institute Kalkaji and I am learning AI engineering course. I am learning various modules in my course.My experience is very good here. IFDA provides practical as well as theory classes of every concept. Even the smart classes are provided for practical section through which it becomes very easy to understand.
Hey ,there I am a student in IFDA Institute and my course is a AI engineering. I am having a great time doing this Course and the best institute. Literally, here the teachers are very good and helpful to the students.
I study at IFDA Institute. I am an AI engineering Course student. I understand everything in my class. It's very easy to understand everything. Every query or question are solved. Very helpful and supportive staff. My experience is good so far with ifda. I suggest everyone to join this institute❤
0k +
0k +
0+
.webp)
0+
Frequently Asked Questions
No. The course starts from basic computer skills and Python. We teach only the math you will actually use in projects.
32 weeks, 320 hours total. Plan for 10 hours per week: 6 hours class, 2 hours hands-on lab, 2 hours supervised project clinic.
Yes. The schedule is designed to fit around other commitments. Weekend and evening batches can be organized.
Any recent laptop with 8 GB RAM (16 GB recommended). A dedicated GPU is helpful but not mandatory; cloud notebooks are provided.
Yes. You will work with modern APIs for function/tool calling, embeddings, RAG, and agent workflows.
Rubrics cover correctness, code quality, documentation, evaluation metrics, and reliability. Capstone includes a live demo.
.png)
 (1).png)
 (1).png)
 (1).png)
 (1).png)
 (1).png)
Get free counselling by our experience counsellors. We offer you free demo & trial classes to evaluate your eligibilty for the course.
Have you
Any question
Or need some help?
Please fill out the form below with your enquiry, and we will respond you as soon as possible.
