1st lesson free
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Dr Kavya Saxena
- Rate L507
- Response 1h
L507/hr
Contact
1st lesson free
- Electrical Engineering
PhD graduate from IIT Kanpur offering research-focused mentoring in ML, LLMs, and Speech Processing.
- Electrical Engineering
Lesson location
About Dr Kavya Saxena
I am a PhD graduate from IIT Kanpur with research experience in Machine Learning, LLMs, Speech Processing, and AI. I enjoy helping students understand complex concepts, explore research, and build the confidence to solve problems independently. I want to share my knowledge because I believe good guidance can make research much more approachable and rewarding.
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About the lesson
- Bachelor's Degree
- Master's Degree
- Doctorate
levels :
Bachelor's Degree
Master's Degree
Doctorate
- English
All languages in which the lesson is available :
English
Research-Oriented Machine Learning & AI Mentoring — From Fundamentals to Research Projects
Are you a graduate student, an undergraduate interested in research, or an aspiring researcher looking to go beyond simply learning Machine Learning and AI?
I offer research-oriented mentoring in Machine Learning, LLMs, RAG systems, Speech Processing, and related areas of AI. My goal is not just to teach you how to implement models, but to help you develop the ability to read research papers, formulate problems, design experiments, evaluate models, and turn ideas into meaningful research projects.
# What can we work on?
Depending on your background and goals, we can cover topics such as:
* Machine Learning & Deep Learning — fundamentals, model architectures, training, evaluation, and experimentation
* Large Language Models (LLMs) — transformers, prompting, fine-tuning, evaluation, and practical applications
* Retrieval-Augmented Generation (RAG) — embeddings, vector databases, retrieval strategies, reranking, generation, and RAG evaluation
* Speech Processing & Speech AI — ASR, speech recognition, speech representations, speech-language models, and evaluation
* Research methodology — literature reviews, identifying research gaps, hypothesis formulation, experiment design, and result analysis
* Research papers — how to read papers critically, reproduce experiments, understand methodology, and identify opportunities for improvement
* Practical research implementation — Python, PyTorch, Hugging Face, experimentation, debugging, and building reproducible research pipelines
# How will the classes work?
I don't follow a fixed syllabus for every student. The sessions are tailored to your current level and research goals.
A typical learning path might look like:
1. Understand → 2. Read → 3. Implement → 4. Experiment → 5. Analyse → 6. Research
We first build the necessary conceptual foundation, then study relevant research papers and implement the underlying ideas. From there, we design experiments, compare approaches using appropriate metrics, analyse failures, and discuss improvements.
For students already working on a research project, we can instead focus directly on their problem — for example, understanding a difficult paper, designing an experiment, debugging an implementation, evaluating a model, or deciding how to approach a research question.
# Who is this for?
This is particularly suitable for:
* Undergraduate students who want to get started with AI research
* Master's students preparing for a thesis or research project
* PhD students looking for technical or methodological guidance
* Students preparing for research internships
* Anyone who wants to move beyond tutorials and understand how modern AI research is actually done
You don't need to be an expert already. What matters is having the curiosity and willingness to learn.
# What makes my approach different?
I believe research is best learned by doing, not by memorising concepts.
Rather than simply explaining a model and moving on to the next topic, I encourage students to ask:
Why does this method work? What are its limitations? How should we evaluate it? What happens if we change something? Why did the experiment fail? Can we design a better experiment?
The aim is to gradually make you independent in solving research problems — so that eventually you can read a new paper, understand its contribution, reproduce its methodology, and formulate your own ideas without depending on step-by-step guidance.
Whether you are just exploring AI research or already have a specific project in mind, the sessions can be structured around your goals, background, and research interests.
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Recommendations
Recommendations come from relatives, friends and acquaintances of the teacher
1
I’ve found Kavya to be an excellent mentor & researcher with a flair for explaining complex concepts with both depth & precision. She is quick in grasping the underlying doubts of her mentees & can teach depending upon where the learner is at in his/her journey. I’m glad to have had the experience of learning from her & getting the much needed guidance & clarity at the right time in my academic endeavours. I strongly recommend her & wish her all the best!
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Rates
Rate
- L507
Pack prices
- 5h: L2,028
- 10h: L4,225
free lessons
The first free lesson with Dr Kavya Saxena will allow you to get to know each other and clearly specify your needs for your next lessons.
- 45mins
online
- L507/h
Details
Cancellation fee of the classes : 1000/-
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