Trainers at ExcelR are passionate about training, and carry 12+ years of industry experience.
Sharpen your skills by engaging in coding contests that involve collaborating with your peers.
Your placement preparation starts from Day 1 and prepares you for guaranteed job interviews across our 2000+ partner companies until you receive your first job offer.
Learners will work on real-life scenarios from various domains to get application knowledge.
Python
Machine Learning (ML)
Large Language Models (LLMs)
Generative AI
PyTorch
TensorFlow
Deep Learning
Anaconda
Jupyter Notebooks
Pandas
NumPy
Python
Seaborn
Matplotlib
Scikit-learn (or sklearn)
Keras
PyTorch
TensorFlow (or TF)
Pillow (PIL
YOLO
OpenCV
Gensim
Hugging Face (Transformers)
SpaCy
NLTK
Generative Pre-trained Transformer (GPT)
LangChain
Build an LLM-powered chatbot that remembers past conversations using LangChain Memory. Focus: Prompt Engineering, Memory Management.
Create a specialized QA system that answers questions based only on a custom document corpus (PDF/Text) using LangChain and a Vector Database. Focus: RAG, Vector DBs, Data Indexing.
Develop a Python script using an LLM API to analyze and summarize complex corporate annual reports into key metrics. Focus: Data Extraction, Advanced Prompting.
Implement and fine-tune a Stable Diffusion model using cloud resources to generate high-quality images based on text prompts. Focus: Diffusion Models, GPU/Cloud Computing.
Use LoRA/PEFT techniques to fine-tune a small LLM model on a narrow dataset (e.g., medical FAQs) to improve its accuracy in a specific domain. Focus: Fine-Tuning, PEFT.
Full-Stack AI Application Deployment (Capstone) All Modules 30 Deploy a fully functional web application (e.g., a creative writing assistant) powered by a hosted LLM, including a front-end UI and a scalable back-end API. Focus: Full Stack Deployment, MLOps.
A financial LLM is providing factually incorrect investment advice. Design and implement a robust RAG system and a zero-shot prompt template to minimize hallucinations. Test: RAG Implementation.
A major e-commerce company wants to replace its flow-chart chatbot with an AI Agent. Design an Agentic chain using LangChain that can dynamically decide whether to look up a refund policy, check stock, or escalate to a human. Test: Agent Chain Development.
A content farm needs to generate 1,000 unique articles daily. Propose a scalable, cost-effective Generative AI workflow, including the choice between an expensive API and a self-hosted, fine-tuned smaller model. Test: Cost/Benefit Analysis, PEFT Strategy.
Junior AI Engineer / Prompt Engineer,Rs6.0 L – Rs12.0 L
Generative AI Specialist / NLP Engineer,Rs12.0 L – Rs20.0 L
Senior AI/ML Engineer,Rs20.0 L – Rs35.0 L
AI Solutions Architect,Rs30.0 L – Rs50.0 L
Principal Data Scientist,Rs45.0 L – Rs70.0 L+
VP / Head of AI & Research,Rs60.0 L – Rs1 Cr+
Get Trained (Live Mentorship)
Submit Assignments (Daily Practice)
Work on Live Industry Projects
Master Case Studies & Analytics
Portfolio & Bootcamps
Career Support
Book Your Slot & Unlock Zero - Cost EMI Plans! Talk to a Counselor Today.
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