You will get state of the art Machine Learning Model


Project details
You will get state of the art machine learning operations in various application areas such as natural language processing, image classification, image clustering, and others using python, tensorflow, scikit learn, numpy, pandas, matplotlib, keras.
What's included
| Service Tiers |
Starter
$80
|
Standard
$250
|
Advanced
$600
|
|---|---|---|---|
| Delivery Time | 7 days | 4 days | 2 days |
Number of Revisions | 1 | 4 | Unlimited |
Number of Model Variations | 1 | 4 | 10 |
Number of Scenarios | 1 | 3 | 6 |
Number of Graphs/Charts | 0 | 0 | 0 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$60 - $150
Additional Revision
+$10
Additional Model Variation
(+ 2 Days)
+$50
Additional Scenario
(+ 2 Days)
+$100
Model Documentation
(+ 2 Days)
+$10
Data Source Connectivity
+$50
Source Code
+$300
3 reviews
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DA
Dionizas A.
Sep 11, 2023
Write a python program to transform natural language to json with specified structure
Despite the fact that William deciding solving the problem in different way and using different approach, the result we got will be used in later iterations. Overall I would like to say Thank to William for the communication, being open and responsive.
NN
Nurman N.
Jul 12, 2023
Transformers Evaluation on English Language Learning Competition datasets
JM
Jon M.
Mar 7, 2022
Instagram scraping task
Great working with William - highly recommended!
About William
AI, LLM, Data Science and Machine Learning Engineer /w Finetuning Exp
Ibadan, Nigeria - 5:41 am local time
Steps for completing your project
After purchasing the project, send requirements so William can start the project.
Delivery time starts when William receives requirements from you.
William works on your project following the steps below.
Revisions may occur after the delivery date.
Preprocessing of dataset
This is the initial preprocessing step of the dataset.
Feature Extraction from Dataset
This is the second step in the project where numerical insights are extracted from the data.