You will get End-to-End Machine Learning and NLP Model Development
Project details
I specialize in building high-accuracy Machine Learning and Natural Language Processing (NLP) models tailored to real-world data. As a researcher and ML engineer, I combine deep technical skills with scientific precision—delivering clean, documented, and production-ready solutions. Whether it’s text classification, sentiment analysis, or model optimization, I ensure your data is transformed into actionable insights through reliable, explainable, and scalable AI systems.
Machine Learning Tools
Azure Machine Learning, Keras, NLTK, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$15
|
Standard
$25
|
Advanced
$35
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 3 | 5 | 8 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code | - |
About Ramez
ML/NLP Engineer
Cairo, Egypt - 3:39 am local time
Key Skills & Expertise:
Machine Learning: Supervised & unsupervised learning, regression, classification, clustering, model evaluation, feature engineering.
Natural Language Processing (NLP): Text preprocessing, sentiment analysis, topic modeling, named entity recognition, text classification, embeddings (Word2Vec, GloVe, BERT, etc.).
Deep Learning: Neural networks, LSTM, GRU, Transformers, attention mechanisms, fine-tuning pre-trained models.
Programming & Tools: Python, Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Hugging Face Transformers, NLTK, SpaCy.
Data Visualization & Analysis: Matplotlib, Seaborn, Plotly, exploratory data analysis, reporting insights.
Deployment & Production: Model optimization, REST APIs for ML models, cloud deployment (AWS, GCP, or Azure).
Projects & Achievements:
Developed an exercise recognition system using smartphone sensor data, achieving high accuracy with combined datasets.
Built sentiment analysis pipelines for product reviews and social media content using both classical ML and transformer-based models.
Created automated NLP workflows for text preprocessing, feature extraction, and classification tasks.
Contributed to research papers in machine learning and NLP, demonstrating practical and theoretical expertise.
I thrive in data-driven environments, enjoy transforming complex datasets into actionable insights, and continuously explore cutting-edge NLP techniques to deliver innovative solutions.
Steps for completing your project
After purchasing the project, send requirements so Ramez can start the project.
Delivery time starts when Ramez receives requirements from you.
Ramez works on your project following the steps below.
Revisions may occur after the delivery date.
Data Collection & Understanding
I’ll review the dataset and project requirements, explore the data structure, and clarify goals before preprocessing.
Data Preprocessing & Cleaning
Clean, tokenize, and prepare text data for model training using Python (NLTK/spacy/pandas).