You will get best machine learning model
Project details
A machine learning model is a concise overview of the goals, methods, and outcomes of a machine learning project. It provides a high-level overview of the project for stakeholders and decision-makers who may not have a technical background. Here's an example of what a project summary for a machine-learning model
Machine Learning Tools
Keras, NumPy, OpenCV, pandas, Python Scikit-Learn, PyTorch, R, scikit-learn, SciPyWhat's included
| Service Tiers |
Starter
$150
|
Standard
$400
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 3 days | 6 days | 8 days |
Number of Revisions | 2 | 2 | 2 |
Number of Model Variations | 2 | 1 | 2 |
Number of Scenarios | 2 | 2 | 1 |
Number of Graphs/Charts | 5 | 3 | 1 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
About Nitish
AI Ml developer
Pune, India - 5:58 am local time
customer demands. Results-driven professional with good technical skills,
firm grasp of business needs and understanding of user requirements.
Assesses troubleshoots problems and conducts tests. Excellent
communication and planning abilities. Very skilled in performing and
reviewing mathematical analysis and testing of high-complexity products,
and applying appropriate mathematical equations.
I have worked with OpenCV, for image-processing, extracting skin, hair color, face shape. I have worked with dlib's module and facial key-point extraction. I have experience on training custom object detection, object classification, Face recognition.
Steps for completing your project
After purchasing the project, send requirements so Nitish can start the project.
Delivery time starts when Nitish receives requirements from you.
Nitish works on your project following the steps below.
Revisions may occur after the delivery date.
Define the problem
Define the problem you are trying to solve, including the specific goals and objectives of the machine learning model. This involves identifying the target variable and the input features.
Collect and prepare the data
Collect the data needed to build and test the model. This may involve data cleaning, data transformation, feature engineering, and data splitting into training, validation, and testing sets.