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Swati Singh
8+ years in AI & Data Science | Expert in Machine Learning, Deep Learning, PyTorch, and LLMs | Turning complex data into accurate predictions
🗣️ Languages: English, Hindi
How this expert can help
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Assignment Planning
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Research Guidance
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💻
Project Support
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✅
Revision Support
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Deadline Help
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Chat with Expert
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About this expert
Hello! I’m Swati Singh
Hello! I’m Swati Singh, an AI and Data Science specialist with 8+ years of experience building data driven solutions for students and academic teams. I help learners succeed with assignment help, homework help, project help, and lab task help by combining strong fundamentals with practical, industry aligned implementations.
From predictive modeling and feature engineering to deep learning, NLP, and computer vision, I focus on clear problem solving, correct evaluation, and clean deliverables. If you need online class help, I can also guide you through concepts step by step so you understand the approach, not just the final output.
AI and Data Science Services for Students
I support a wide range of academic work across ML and data analytics. My goal is to make your submission accurate, well structured, and aligned with your course requirements.
- ✅ Predictive modeling for regression and classification tasks, including performance tuning and error analysis
- ✅ Machine Learning projects using scikit learn, pipelines, and robust evaluation metrics
- ✅ Deep learning solutions using PyTorch and neural network architectures
- ✅ NLP work including text preprocessing, embeddings, transformers, and LLM based workflows
- ✅ Computer Vision tasks such as image classification, detection workflows, and model training strategies
- ✅ Data mining, statistical analysis, and big data analytics concepts using Hadoop and Spark style approaches
Core Expertise in Trending AI Topics
Modern AI requires more than basic models. I specialize in advanced, high ranking areas that are frequently used in coursework and research projects.
- 🧠 Generative AI with GANs and Transformers for creative and analytical tasks
- 🧩 Large Language Models including prompt design, evaluation, and practical LLM integration concepts
- ⚙️ MLOps mindset for reproducible experiments, model versioning, and deployment readiness
- 📊 Feature engineering and data engineering practices that improve model accuracy and stability
- 🛡️ AI ethics and fairness considerations to support responsible ML outcomes
How I Deliver Your Homework Help and Projects
I work with a clear academic workflow. You get solutions that are easy to review, easy to run, and easy to explain during presentations or viva.
- 🧾 Problem understanding: I analyze your prompt, rubrics, and required methods so the solution matches expectations
- 🧠 Model selection: I choose the right algorithm for the dataset and task, including supervised and unsupervised learning
- 🔧 Implementation: I write clean Python code and use SQL and data pipelines when needed
- 📈 Evaluation: I include metrics, validation strategy, and meaningful insights from results
- 📝 Documentation: I provide step by step explanations so you can learn and reuse the approach
Tools and Technologies I Use
Whether you are working on a lab task, an online class project, or a final submission, I support the tools your course expects.
- 🐍 Python for Data Science, including NumPy, pandas, and ML utilities
- 🧱 PyTorch for deep learning training and experimentation
- 📚 TensorFlow and Keras when your syllabus requires it
- 📌 Scikit learn for classical ML, preprocessing, and model pipelines
- 🗄️ SQL for data analysis, querying, and feature extraction
- ☁️ Cloud AI concepts such as AWS SageMaker, Google AI, and Azure ML
My Learning Approach for Strong Grades
I don’t just provide answers. I help you understand the logic behind predictive modeling, deep learning, and LLM workflows. My tutoring style is designed to build confidence, improve your problem solving, and help you score better on tests and practical evaluations.
- 🎓 I teach core concepts like training, validation, overfitting, and model interpretability
- 🧪 I explain how to choose evaluation metrics for each task, including classification and regression
- 🧠 I guide you through feature engineering and data preparation for better results
- 🔍 I help you present findings clearly with charts, summaries, and actionable insights
Let’s work together! Send your topic, dataset details, and deadline. I’ll help you move from confusion to a strong, well explained AI and Data Science deliverable that supports your academic success.
Expertise areas
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Education & credentials
Academic background and certifications added to this expert profile.
Education
Postgraduate Diploma in Big Data Analytics
Gutkowskiton University
2018
Certifications
TensorFlow Developer Certificate
2018
Student reviews
Feedback is shown only after moderation. New expert profiles may not have public reviews yet.
4.4
★★★★☆
Based on 24 reviews
5 star
16
4 star
4
3 star
2
2 star
1
1 star
1
M
Verified Student
03-07-2026
★★☆☆☆
Low effort shown.
M
Verified Student
20-06-2026
★★★☆☆
Okay but needs refinement.
M
Verified Student
20-06-2026
★★★★☆
Good timely delivery Swati Singh.
M
Verified Student
13-06-2026
★★★★★
Excellent academic assistance Swati Singh.
s
Verified Student
25-05-2026
★★★★★
Excellent final submission quality.
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Can I chat with this expert before hiring?
Yes. You can start a chat first, explain your assignment, share important details, and ask whether the expert is suitable for your task.
Can I upload assignment files or project instructions?
Yes. You can share rubrics, PDFs, screenshots, datasets, code files, and other required documents during the hiring or chat process.
How does payment work?
Payments are handled through the platform so your order, wallet activity, invoices, and project status remain organized in your student dashboard.
Can I request changes after delivery?
Yes, you can request revisions based on your original requirements and feedback. Clear instructions help the team resolve revision requests faster.
What should I send before hiring?
Send the deadline, marking rubric, question file, expected format, word count or technical requirements, and any instructor notes.
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