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I will develop machine learning, deep learning and computer vision solutions
Software Engineer, Python Automation, Web Development and Debugging
I'm a Python-based AI Engineer & Data Scientist specializing in Machine Learning, Deep Learning, and Computer Vision. I build end-to-end pipelines using PyTorch, TensorFlow, and Scikit-learn — from da...
About this Gig
Looking for a custom solution in Machine Learning, Deep Learning, or Computer Vision?
I am a Python Data Scientist & AI Engineer specializing in end-to-end ML pipelines, Computer Vision systems, and predictive modeling. I build robust models with clean, documented code.
My Services:
- Computer Vision: Object Detection (YOLOv8), Image Classification (CNN, ResNet), Segmentation & OpenCV.
- Deep Learning: Neural Networks with PyTorch, TensorFlow & Keras.
- Predictive Modeling: Classification, Regression & Time-Series Forecasting using XGBoost, LightGBM & Scikit-learn.
- Data Preparation: Cleaning, EDA, Feature Engineering & Visualizations.
- Model Evaluation: Hyperparameter Tuning, Confusion Matrix & ROC-AUC.
Tech Stack:
Python | PyTorch | TensorFlow | OpenCV | Scikit-learn | YOLO | Pandas | NumPy
Why Choose Me?
- Clean, modular Jupyter Notebook (.ipynb).
- Reliable models with proper validation & zero data leakage.
- Fast turnaround & post-order consultation.
Programming language:
Python
•
MATLAB
•
Colab
Frameworks:
Scikit-learn
•
Keras
•
PyTorch
•
Panda
APIs:
Microsoft Computer Vision AI
Tools:
Jupyter Notebook
•
OpenCV
•
TensorFlow
•
MLflow
•
SimpleCV
•
Colab
FAQ
What will you deliver upon project completion?
You will receive a clean, well-commented Jupyter Notebook (.ipynb), the trained model weights/files (.pkl, .h5, or .pt), and performance evaluation graphs.
Can you train custom Computer Vision models like YOLO or CNN on my images?
Yes! I can annotate, preprocess, augment, and train custom YOLOv8 or CNN architectures on your proprietary dataset for classification, object detection, or tracking.
What if I don't have a cleaned dataset yet?
I handle raw and noisy datasets, including missing value imputation, categorical encoding, feature scaling, and exploratory data analysis (EDA).
