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Fiverr Pro
Nederlands
$
USD
I will do machine learning and ai projects
Hi! I am a Data Scientist and Machine Learning Engineer dedicated to turning complex data into scalable, intelligent solutions. I specialize in building end-to-end ML pipelines, deep learning architec...
Over deze dienst
I provide professional machine learning, AI, and data analysis services using Python. I can help with classification, predictive analysis, clustering, model optimization, data preprocessing, visualization, and research-based ML projects. Whether you need a college project, business solution, or custom AI implementation, I deliver clean, efficient, and well-documented code with clear communication and fast delivery. My goal is to provide accurate, optimized, and reliable machine learning solutions tailored to your requirements.
Programmeertaal:
Python
Frameworks:
Scikit-learn
•
keras
•
PyTorch
•
Panda
Tools:
Jupyter-notitieboek
•
opencv
•
tensorflow
•
Excel
•
Colab
Veelgestelde vragen
In what format will the final project be delivered?
I deliver the complete source code in highly organized, well-commented Jupyter Notebooks (.ipynb) or standard Python scripts (.py), making it incredibly easy for you to run, review, and modify the workflow.
Do I need to provide the dataset, or can you source one?
You will need to provide the dataset or a clean sample of the data you want to use. If you do not have data yet but have a specific project goal, please message me first so we can discuss potential public data sources.
What is your policy on revisions?
Revisions cover adjustments to model parameters, changing optimization metrics, or tweaking existing code logic. Revisions do not cover swapping out the original dataset for a completely new one midway through the order.
Can you build Deep Learning neural networks, or just basic machine learning algorithms?
I handle both. For standard tabular data, I typically use highly efficient algorithms like Random Forest or XGBoost. For more complex datasets that require deep learning, I can design and train custom neural networks using TensorFlow and Keras.
What happens if the final model accuracy is low?
Machine learning depends heavily on the quality of the data. If the initial model's performance isn't great, I will use advanced techniques like feature engineering, testing alternative algorithms, and hyperparameter tuning to squeeze out the highest possible score.
What if my dataset is a complete mess? Do I need to clean it first?
Not at all. Real-world data is rarely perfect. Data cleaning and preprocessing like handling missing values, filtering out noise, and formatting columns are fully included in every package before I ever start training a model.
