You will get any kind of machine learning, computer vision project
Project details
Our project aims to leverage the power of machine learning, computer vision, and deep learning to solve real-world problems. We will be using a variety of algorithms and techniques, including neural networks, convolutional neural networks, and support vector machines, to analyze and process large amounts of data. The goal is to extract valuable insights and make predictions or decisions based on the data. We will be using state-of-the-art tools and libraries, such as TensorFlow and scikit-learn, to implement and evaluate our models. The end result will be a highly accurate and efficient system that can be used to improve business processes, optimize resource allocation, or enhance decision-making.
Machine Learning Tools
ChatGPT, Google Data Studio, MATLAB, pandas, Python, scikit-learn, TensorFlowWhat's included
| Service Tiers |
Starter
$20
|
Standard
$60
|
Advanced
$150
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 13 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 3 | 4 |
Number of Scenarios | 1 | 1 | 2 |
Number of Graphs/Charts | 2 | 3 | 5 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$10 - $50
Additional Revision
+$5
Additional Model Variation
(+ 1 Day)
+$10
Additional Scenario
(+ 2 Days)
+$10About Md Parvez
Full Stack | SaaS | Ai
Savar, Bangladesh - 12:49 pm local time
Steps for completing your project
After purchasing the project, send requirements so Md Parvez can start the project.
Delivery time starts when Md Parvez receives requirements from you.
Md Parvez works on your project following the steps below.
Revisions may occur after the delivery date.
Define the problem
The first step is to define the problem that you are trying to solve using machine learning. This includes identifying the business needs, defining the goals and objectives, and understanding the data that is available to you.
Explore and prepare the data
The next step is to explore and prepare the data that you will be using for your machine-learning model. This may involve cleaning and preprocessing the data, as well as selecting a subset of the data to use for training and testing.