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I will build custom nlp and sentiment analysis models in python
AI and ML Engineer
I am a Machine Learning Engineer with experience developing machine learning, deep learning, computer vision, and natural language processing (NLP) solutions using Python. I build reliable models for ...
Over deze dienst
Are you looking to analyze text or audio data, transcribe speech, or build custom AI classification models? You are in the right place!
I am a Machine Learning Engineer specializing in NLP, Text Analytics, and Audio AI in Python. I build end-to-end pipelines tailored to your dataset using modern text and speech libraries.
What I Offer:
- Text & Audio Preprocessing: Tokenization, lemmatization, stop-word removal, cleaning, and audio signal processing.
- Speech & Audio NLP: Audio transcription using OpenAI Whisper, audio feature extraction (MFCCs/Spectrograms with Librosa), and voice data analysis.
- Sentiment Analysis: VADER rule-based sentiment, customer review scoring, and social feedback analysis.
- Text Classification: Topic categorization, spam detection, and intent recognition using Scikit-Learn algorithms (Naive Bayes, Random Forest, XGBoost).
- Local API Integration: Packaging your text or audio pipeline into a lightweight FastAPI endpoint (app.py).
Tech Stack:
- Language: Python
- Libraries: Whisper, Librosa, Scikit-Learn, NLTK, SpaCy, Pandas
- Environment: Google Colab / Jupyter Notebooks (.ipynb)
- API: FastAPI
Please message me with your project details before placing an order!
Programmeertaal:
Python
Frameworks:
Scikit-learn
•
Panda
•
Overige
API's:
Overige
Tools:
Jupyter-notitieboek
•
Colab
Mijn portfolio
Andere Data science en ML diensten die ik aanbied
Veelgestelde vragen
What formats will I receive my final deliverables in?
You will receive a clean, fully commented Google Colab Notebook (.ipynb) or standalone Python scripts (.py), along with trained model files (.pkl / .joblib), audio processing scripts, and model evaluation metrics.
Can you process speech and audio data alongside text?
Yes! I use OpenAI Whisper for high-accuracy speech-to-text transcription and Librosa for audio feature extraction (MFCCs, spectrograms). I can feed transcribed voice data directly into sentiment models like VADER or custom text classifiers.
Do you provide cloud deployment for models or audio pipelines?
No, I do not provide cloud hosting (AWS/GCP/Heroku). However, I can package your complete text or audio processing model into a clean, lightweight local FastAPI script (app.py) ready for local integration.
What dataset or file formats should I provide?
For text data, I accept CSV, JSON, Excel, or TXT formats. For audio processing, you can upload WAV, MP3, or FLAC files directly or share a Google Drive download link.
