You will get predictive machine learning model that can be integrated into other tools
Project details
You will get a powerful and reliable predictive machine learning algorithm for a particular data context. I will gladly include additional features and metrics on the original data as well as on the model's output if I find or devise any that are valuable.
If you have a baseline predictive model or algorithm, I will meet or exceed it's overall performance with greater sensitivity and recall.
I place great emphasis on model interpretability so the model is understandable and trustworthy.
The final deliverable for the base project is the trained model file and source code. Model and code documentation are included.
Missing something? Feel free to reach out and let's see if I can provide a better service offering for you.
If you have a baseline predictive model or algorithm, I will meet or exceed it's overall performance with greater sensitivity and recall.
I place great emphasis on model interpretability so the model is understandable and trustworthy.
The final deliverable for the base project is the trained model file and source code. Model and code documentation are included.
Missing something? Feel free to reach out and let's see if I can provide a better service offering for you.
Machine Learning Tools
ChatGPT, Google AutoML, Google Sheets, GPT-3, H2O, Keras, Microsoft Excel, MLflow, NumPy, pandas, PyMC, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, Sonnet, SQL, TensorFlow, TextBlob, Vertex AI, XGBoostWhat's included $750
These options are included with the project scope.
$750
- Delivery Time 7 days
- Number of Revisions 2
- Number of Model Variations 2
- Number of Scenarios 1
- Number of Graphs/Charts 4
- Model Validation/Testing
- Model Documentation
- Data Source Connectivity
- Source Code
Optional add-ons
You can add these on the next page.
Fast 4 Days Delivery
+$200
Additional Revision
+$400
Additional Model Variation
(+ 2 Days)
+$200
Additional Scenario
(+ 7 Days)
+$800
Additional Graph/Chart
(+ 1 Day)
+$100
Google Cloud or Render Deployment
(+ 1 Day)
+$300
Dashboard
(+ 2 Days)
+$800
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About Austin
Engineer: Software Development, Data Science, and ML
Brandon, United States - 9:28 pm local time
I can help implement:
- various types of automation, including quality assurance automation
- certain cloud solutions with GCP, AWS, and Microsoft AzureML
- data transformations
- machine learning models
- dashboards
- command-line interfaces
- financial analyses
- spreadsheet solutions (Google Sheets and Excel)
- various types of interactive visualizations
- software modules (in particular, I'm currently learning to build Python modules in Rust for faster performance)
I have formal training as an engineer up to the Master's level. I also have training from past full-time roles as research engineer and data analyst. I attribute much of my current skills to ongoing self-study using online resources such as Packt and O'Reilly technology and business training.
As a research engineer, I developed experimental machine learning models with Python and wrote corresponding technical reports. These efforts were also the subject of my graduate work. As a data analyst, I collected and analyzed data from solar energy infrastructure projects and conducted external market research to determine future project viability in different regions.
Since joining Upwork, I have assisted clients with various ML and data engineering tasks. As mentioned earlier, I am currently training to be a full-stack solutions architect with both coding and strategic planning offerings.
Steps for completing your project
After purchasing the project, send requirements so Austin can start the project.
Delivery time starts when Austin receives requirements from you.
Austin works on your project following the steps below.
Revisions may occur after the delivery date.
Due diligence on problem domain
I will spend a reasonable amount of time to understand your problem's domain via my own research as well as any documentation you provide. This greatly facilitates the ML life-cycle from choice of model, feature engineering, implementation, etc.
Exploratory data analysis