I will build ml model using machine learning, deep learning with python data science ai
AI and Data Science Solutions in ML, Gen AI, NLP and Computer Vision
About this Gig
Building a Machine Learning model is easy; making it perform on real-world data is the hard part.
We have experience of 6 years offering solutions for machine learning, deep learning and data science domain using python.
Machine Learning Solutions:
- Supervised machine learning
- Classification
- Regression
- Clustering
- Text classification
- Data Analysis
- Semantic Analysis
- Neural Networks
- Time series Analysis
- Time series forecasting
- Hyper Parameter tuning
- and much more using machine learning techniques
Deep Learning Solutions:
- Convolutional Neural Network
- ANN, RNN
- Basic Deep Learning Projects
- Image classification
- object detection using deep learning models
- object segmentation
- Datasets annotations
- Face based projects using deep learning models
- Face detection and recognition
- Long Short Memory(LSTM)
- Vehicle Analytics
Tools
- Python
- Juypter Notebook
- Colab
- VS Code
IMPORTANT NOTE:
Please discuss requirements before placing the order.
Charges will varies based on the complexity of the problem, specifications required, and accuracy expectations.
Thanks and Regards,
Muhammad Umair
My Portfolio
FAQ
What do I need to provide to get started?
I simply need your dataset (CSV, Excel, or SQL file) and a clear description of your goal (e.g., "I want to predict if a customer will churn based on this data").
Do you provide the Source Code?
Yes! All packages include the clean, commented Python source code (Jupyter Notebook or .py file) so you can run and verify the model yourself.
Can you deploy the model as an App?
Yes, in the Premium package (or as a custom offer), I can deploy your model using Streamlit so you can interact with it via a web interface without writing code.
Which Python libraries do you use?
I use industry-standard libraries including Pandas, NumPy, Scikit-Learn, TensorFlow, Keras, Matplotlib, and Seaborn.
What is the difference between Classification and Regression?
Classification predicts a category (e.g., "Yes/No", "Spam/Not Spam", "Cat/Dog"), while Regression predicts a continuous number (e.g., "Price", "Temperature", "Sales Revenue"). I can handle both types of problems.
When should I choose Deep Learning over standard Machine Learning?
Standard ML (Random Forest, SVM) is great for structured data (Excel sheets). Deep Learning (Neural Networks) is better for complex data like Images, large scale Time-Series, or when you need higher accuracy on massive datasets.

