Data Scientist at Accenture (Previously at XYZ)
Today was my first day at IIT Hyderabad and making it here as the youngest learner was only possible because of Prudhvi’s mentorship. His way of simplifying concepts and analogies made it easier to recall. Happy to have undertaken this course and this course helped me make my IT dream come true.
Data Scientist at Tech Mahindra (Previously at XYZ)
As a student of the NLP 100 Hours program, I can say, we got the best instructor. Great case studies, exhaustive hands-on, Good interactivity sessions, and a diverse batch for good networking are the best part of the course. This is the best course for NLP
Sr. Engineer at Accenture (Previously at XYZ)
I have successfully made a career transition into DL. Thanks to lively teaching and practical examples which helped me solve any problems and improved my problem-solving capabilities. The data tales provided were some of the best things I have ever seen.
Data Science Leader
I can recommend Prudhvi as a enthusiastic Data Scientist, who is not afraid to dig in most complex algorithm logic, finding a way how to implement new techniques or fix some cumbersome code issue.
Sr Manager, Data Science
Prudhvi’s indepth knowledge, is evident in teaching as he seamlessly synchronizes with students at all stages. While his structured live classes sink in concepts, his practical examples , hands-on practice helps to retain them!
Senior Data and Analytics Manager
The training provided by Prudhvi is a uniquely comprehensive curriculum, one of the best I have come across. Perfectly balances fundamental of Math and Statistics, gradually progressing into complex ML , Gen AI, making the transition seamless
Data Science Leader
I recommend Prudhvi for his approach to deliver the content is amazing & updated with market requirements. The assignments given are very well drafted that will test your knowledge. I thoroughly enjoyed his classes.
This intensive course equips you with the essential mathematical building blocks for data science success.
In this module, learners will understand language modelling , instruction-tuned models, Agents, successful agentic use cases in enterprises, best practises, Multi Agentic Systems
In this Module, learners will learn generation across open and proprietary models — greedy decoding, top-p and top-k sampling , determinism in production, zero-shot and few-shot prompting, chain-of-thought, tree-of-thought, and self-correction/Reflexion patterns for harder reasoning tasks.
In this session, learners will get deep understanding of word and subword tokenisation — byte pair encoding, WordPiece, SentencePiece — and why tokenisation changes both cost and behaviour at scale.
In this session, transformer block itself: self-attention, multi-head attention, positional encoding, and auto regressive generation , ROPE Attention & Modern variants of Transformer
In this Module, dense retrieval and grounded generation, chunking strategies, bi-encoders & cross-encoders for reranking. vector databases, retrieval design decisions , Agentic RAG, Multi hop & Modern RAG versions
This module helps learners move from baseline RAG into Graph RAG, HyDE, Agentic RAG, Corrective RAG, and Self-RAG, along with the system design considerations behind each. Covers evaluation seriously — matching and retrieval metrics, plus LLM-as-a-judge for faithfulness, groundedness, and hallucination detection.
Covers supervised fine-tuning with current recipes, adapters, and low-rank adaptation, plus quantisation formats including GPTQ, AWQ, and GGUF, and serving with vLLM. Introduces Mixture-of-Experts architectures and the active-versus-total-parameter trade-off, then reinforcement learning for LLMs through DPO, GRPO, and preference learning.
This module covers short-term, long-term, episodic, and semantic memory, reflection mechanisms, and planning approaches including Plan-and-Execute, ReWOO, Tree of Thoughts, and Graph of Thoughts. Implements all of it in LangGraph — nodes, edges, state, sequential, parallel, conditional and iterative workflows, and persistence.
This covers agent evaluation, tracing and observability, guardrails, prompt injection defences, tool security, human-in-the-loop approval, and cost optimisation across deployment patterns. Applied end-to-end through enterprise builds such as an HR support agent and a clinical information assistant using agents, MCP, and tools.
Hugging Face, Langchain, Langgraph, Langfuse, RAGAS, Cohere, MCP, Cohere, etc
Enterprise Health care Assistance, Enterprise HR chatbot etc
By the end of this course, you’ll be empowered to confidently approach AI Agents with a solid understanding of the core concepts that fuel AI
We're so confident in our program, that we offer a 100% placement guarantee
Learn from structured content developed by seasoned industry experts, ensuring you gain the most relevant and practical data science skills.
Master data science at a competitive price. We offer structured learning without breaking the bank.
Join a community of over 500 data science success stories – our proven approach gets results!
You’re interested in data science but overwhelmed by the vast amount of information and jargon. You lack the foundational knowledge to make sense of it all.
Probability, statistics, and linear algebra sound intimidating, holding you back from pursuing a data science career.
You collect data but struggle to interpret it effectively. You’re unsure how to find meaning and draw valuable insights from your datasets.
You want to transition into a data-driven field but lack the mathematical skills to compete for data science jobs.
You don’t need any prior experience or knowledge to learn data science
While a solid foundation in math can be helpful, it’s not a barrier to entry.
Build Clarity & Confidence:
You will know much better about the field of Data Science and understand whether this is a fit for you or not.
Plan Your Path Forward:
Understand the next steps to advance in data science, whether through further study, personal projects, etc.
