I will build machine learning, deep learning, and neural network models and projects
A Professional Programmer
About this Gig
As an AI Engineer with 2 years of hands-on experience, I build robust ML/DL models and end-to-end Python pipelines tailored to your dataset and requirements.
What I Offer:
- Data Analysis & Preprocessing: Data cleaning, feature engineering, and exploratory data analysis (EDA).
- Machine Learning Models: Supervised and unsupervised algorithms (XGBoost, Random Forest, LightGBM, SVM).
- Deep Learning & Neural Networks: Custom CNNs, LSTMs, and Transformers using PyTorch and TensorFlow.
- Model Optimization: Hyperparameter tuning, cross-validation, and metrics evaluation (F1-Score, ROC-AUC).
- MLOps & Deployment: REST APIs (FastAPI, Flask), Streamlit web applications, and Docker containerization.
Tech Stack: Python | PyTorch | TensorFlow | Scikit-Learn | Pandas | OpenCV | Hugging Face | Jupyter Notebook
Why Choose Me?
- 2 Years Field Experience: Practical expertise in developing scalable, real-world AI solutions.
- Production-Ready Code: Clean, modular, and thoroughly commented Python scripts (.ipynb or .py).
- Visual Documentation: Clear performance reports, loss curves, and evaluation graphics.
PLEASE MESSAGE ME BEFORE PLACING AN ORDER to discuss your dataset, project scope, and custom requirements!
Other Data Science & ML Services I Offer
FAQ
What information do you need to start my machine learning project?
I need your dataset, project requirements, target variable, desired outcome, and any specific algorithms, metrics, or deliverables you require. If you are unsure about the best approach, I can recommend one based on your problem and dataset.
Can you work with my own dataset?
Yes. I can work with your dataset and handle data cleaning, preprocessing, exploratory data analysis (EDA), feature engineering, model development, and evaluation according to your requirements.
Which machine learning algorithms do you work with?
I work with a wide range of supervised and unsupervised algorithms, including Random Forest, XGBoost, LightGBM, SVM, Logistic Regression, Linear Regression, Decision Trees, KNN, clustering algorithms, and other suitable models.
Can you compare and optimize multiple machine learning models?
Yes. I can train multiple suitable models, compare their performance, perform cross-validation and hyperparameter tuning, and select the best-performing model based on appropriate evaluation metrics.
Do you provide the complete Python source code?
Yes. Source code is included according to the selected package. I provide clean, structured Python code in .py or .ipynb format along with the relevant outputs and documentation.

