You will get a custom deep learning based NLP model for customer/user analysis
Project details
You will get a trained and working NLP model for customer review analysis. Analyzing reviews is crucial for understanding the impact of features on your product. Conducting this kind of analysis manually is pretty expensive, it also takes a lot longer than building a deep learning solution. With deep learning, you can save more money than analyzing with humans while keeping the accuracy.
Therefore, it is beneficial to use the review data for modeling rather than analyze the reviews manually. You just need to have labeled data (I can also collect and build a dataset myself), then I will use the data to train a new model and conduct an analysis with it. The results will be delivered to you as a Power BI dashboard along with the source code.
Therefore, it is beneficial to use the review data for modeling rather than analyze the reviews manually. You just need to have labeled data (I can also collect and build a dataset myself), then I will use the data to train a new model and conduct an analysis with it. The results will be delivered to you as a Power BI dashboard along with the source code.
What's included
| Service Tiers |
Starter
$50
|
Standard
$75
|
Advanced
$100
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 2 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 1 | 2 |
Number of Graphs/Charts | 5 | 6 | 6 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code | - | - |
Frequently asked questions
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AA
Arhum A.
May 14, 2023
Core Python Developer
He is very cooperative and nice guy.
Worked really hard and always delivered on time.
He did better than my expectations and always recommend him.
Worked really hard and always delivered on time.
He did better than my expectations and always recommend him.
DM
Deem M.
Apr 10, 2023
Job task of Data classification by implementing Perceptron Algorithm
CM
Chris M.
Feb 16, 2023
AI Quality Control - Attention to detail, willing to work, up for the grind
Although it was only a small body of work, Alper did a solid job and really appreciated his patience with me!
YN
Yaki N.
Jun 18, 2022
Kaggle colab
5 stars.
Excellent job on Kaggle competition script.
Very fast and generic code
Excellent job on Kaggle competition script.
Very fast and generic code
About Alper
Multimodal ML Engineer | Speech, Vision & LLM Pipelines
Ankara, Turkey - 10:25 am local time
I build real-time multimodal AI systems — voice/video agents, ASR, OCR, diffusion and LLM pipelines — production-ready via FastAPI, Livekit/SGLang/vLLM/LMDeploy. Happy to prototype your next interactive AI solution!
🔧 What I Deliver:
• Live conversational agents with STT → LLM → TTS pipelines using LiveKit and WebRTC, featuring interruption handling and expressive speech
• Emotion-aware engagement with real-time emotion detection and expressive “emote” feedback via RPC
• OCR and vision pipelines for image/question understanding and reasoning integrated into live streams
• Multimodal LLM/VLM workflows using LangChain, diffusion models, referent grounding, table parsing, and document Q&A.
• Production-grade deployment using scalable APIs (FastAPI, Docker, K8s), efficient data pipelines, and low-latency inference (<200 ms)
• PEFT models with LoRA and QLoRA for LLM/VLMs with end-to-end training/inference infrastructures deployed on-premise/cloud.
💡 Highlighted Projects:
• Real-time speech, vision, and text synthesis with under 200 ms latency
• LiveKit-based conversational AI agent with emotion detection and expressive voice/video interaction
• VLM/LLM toolkits for OCR extraction, adaptive pipelines, and diffusion‑model‑based image generation
• Complete training/inference infrastructures at scale.
✅ Ideal For:
• Voice/video-based AI assistants and chatbots
• Emotion-sensitive conversational systems
• Interactive multimodal AI (OCR, ASR, LLM integration)
• Full-stack ML prototypes through to production
📬 Let’s collaborate!
Send me a message with your interactive AI project — quick prototype, optimization, or full deployment. I generally respond within a few hours.
Steps for completing your project
After purchasing the project, send requirements so Alper can start the project.
Delivery time starts when Alper receives requirements from you.
Alper works on your project following the steps below.
Revisions may occur after the delivery date.
I will prepare a script for data collection (if needed)
I will use playwright, selenium, httpx, requests, and beautifulsoup libraries to collect and save the data occasionally.
I will apply an exploratory data analysis
Understanding the data is important, important insights can be used to build much more high performant models. All the exploratory data analysis source code will be sent to you.