You will get Python Developer with AI / Machine Learning Experience
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Project details
Preprocessed and Cleaned Training Data
The raw data collected for the project will be preprocessed and cleaned to remove any inconsistencies, errors, or irrelevant information. This ensures that the data is suitable for training the machine learning models.
Trained Machine Learning Models
Using various machine learning techniques such as random forests, gradient boosting, neural networks, etc., models will be trained on the prepared data. These models are tailored to perform well on the specific task defined by the project.
Model Performance Evaluation
The performance of the trained models will be evaluated using metrics such as accuracy, F1 score, precision, recall, etc. This assessment helps in determining how well the models generalize to unseen data.
Optimized ML
Efforts will be made to optimize the models for both performance and efficiency. This could involve tuning hyperparameters, reducing overfitting, or improving computational efficiency.
Deployed ML
Finally, the optimized and tested machine learning model will be deployed and integrated into the client's platform or application. This ensures that the model is ready to be used in a real-world setting.
The raw data collected for the project will be preprocessed and cleaned to remove any inconsistencies, errors, or irrelevant information. This ensures that the data is suitable for training the machine learning models.
Trained Machine Learning Models
Using various machine learning techniques such as random forests, gradient boosting, neural networks, etc., models will be trained on the prepared data. These models are tailored to perform well on the specific task defined by the project.
Model Performance Evaluation
The performance of the trained models will be evaluated using metrics such as accuracy, F1 score, precision, recall, etc. This assessment helps in determining how well the models generalize to unseen data.
Optimized ML
Efforts will be made to optimize the models for both performance and efficiency. This could involve tuning hyperparameters, reducing overfitting, or improving computational efficiency.
Deployed ML
Finally, the optimized and tested machine learning model will be deployed and integrated into the client's platform or application. This ensures that the model is ready to be used in a real-world setting.
Machine Learning Tools
Azure Machine Learning, ChatGPT, deeplearn.js, GitHub Copilot, Google AutoML, Google Sheets, GPT-3, Keras, Microsoft Excel, Microsoft Power BI, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, Scrapy, SQL, TensorFlow, Tesseract OCR, Theano, Vertex AIWhat's included
| Service Tiers |
Starter
$1,500
|
Standard
$2,500
|
Advanced
$4,000
|
|---|---|---|---|
| Delivery Time | 7 days | 15 days | 30 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$100
Additional Model Variation
(+ 2 Days)
+$200
Additional Scenario
(+ 2 Days)
+$200
Additional Graph/Chart
(+ 2 Days)
+$100
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About Luis Daniel
Vibe Coding Expert | Claude Code | Codex | React & Python
100%
Job Success
Cainta, Philippines - 10:44 am local time
I'm a seasoned Software Engineer who works the modern way: I orchestrate Claude Code and voice/dictation agents to move from idea to working, deployed app in a fraction of the time — while staying hands-on with architecture, code quality, security, and the final result. The result is startup speed without the throwaway code. I own the outcome, not just the keystrokes.
This AI-augmented approach is where I add value. It means I can prototype in hours, iterate with you in real time, and absorb scope changes that would stall a typical dev shop — all while shipping clean, reviewed, maintainable code that another engineer could pick up tomorrow.
I brought Claude Code and agent-driven development into our engineering process and helped the team adopt it — setting the standards, building a playbook the team now uses, and mentoring developers to ship faster without cutting corners. It's the same initiative and ownership I bring to client work.
Across 100+ delivered projects, here's where I bring real depth:
🖥️ WEB & DESKTOP APPLICATIONS
React 18/19, TypeScript, Vite, Tailwind CSS, Zustand and TanStack Query, react-hook-form + Zod validation, React Router. I've shipped 15+ production single-page apps: multi-tenant SaaS dashboards, CRM and admin panels, asset-tracking and field-survey tools, real-time dashboards, and billing-reconciliation apps that compare data across multiple vendors to catch discrepancies automatically. I also build cross-platform desktop apps with Electron (Windows, macOS, Linux), including 3D interfaces with Three.js, offline-first local databases, auto-update infrastructure, and native device integration.
☁️ AZURE & MICROSOFT 365
This is home turf. Azure Functions (Node and Python), Static Web Apps, Microsoft Graph, Entra ID, Intune, Conditional Access, Privileged Identity Management (PIM), GDAP, Key Vault, Azure Automation runbooks, Log Analytics, and Power Platform custom connectors. I design secure, multi-tenant systems with MSAL authentication and end-to-end JWT validation. I've automated identity governance, hybrid Active Directory + Entra sync, device onboarding via Windows Autopilot, license compliance, and security monitoring — the unglamorous plumbing that keeps an organization secure and audit-ready.
🔌 API INTEGRATIONS & AUTOMATION
I connect systems that don't want to talk to each other. I've integrated ConnectWise, Pax8, Microsoft Partner Center, Jira, Smartsheet, Dropsuite, Calendly, Samsara, Paycom, TripShot, Fleetio, and more. This includes serverless, event-driven pipelines; an AI voice-to-ticket system that answers calls, transcribes speech, and opens support tickets automatically; n8n workflow automation self-hosted on Docker with PostgreSQL and Redis; and scheduled monitoring that delivers alerts across Teams, email, and ticketing systems. If it has an API — or even just a clunky export — I can wire it into your workflow.
📊 DATA, ETL & MIGRATIONS
Azure Synapse and Data Factory pipelines, data-lake ingestion, Power BI reporting, and Python ETL with fuzzy matching and deduplication. I've built analytics platforms that unify fleet telematics, payroll, and maintenance data into a single warehouse, maintained large SQL stored-procedure codebases, and run high-stakes platform migrations — including Jira Server to Jira Cloud with full attachment migration and user-account merges — nothing lost, nothing duplicated, clean rollback at every step.
🔧 HARDWARE & IOT
When a project calls for it, I go beyond the browser: Raspberry Pi camera streaming (MJPEG), RFID integration, and serial-port device communication.
💡 HOW I WORK
- Clear, frequent communication — you always know where things stand.
- Honest status. If something is blocked, risky, or a bad idea, you'll hear it early, not after the deadline slips.
- Lean iterations over big-bang reveals. I'd rather show you something working on day two than a polished plan on week two.
- Security and maintainability are non-negotiable, even on fast timelines. AI compresses the work; it doesn't excuse cut corners. Everything gets reviewed and tested.
- I leave every project better than I found it.
WHO I'M A GREAT FIT FOR
Founders and teams who need a working, deployed product quickly. Businesses drowning in manual processes that should be automated. Companies on the Microsoft stack who need someone fluent in both app development and identity/security. Anyone with a "these two systems should just talk to each other" problem.
If you've got an idea, a half-finished project, or a process quietly costing you hours every week — let's talk. Tell me the details and I'll be honest about how I'd approach it and where AI can save you time and money
Steps for completing your project
After purchasing the project, send requirements so Luis Daniel can start the project.
Delivery time starts when Luis Daniel receives requirements from you.
Luis Daniel works on your project following the steps below.
Revisions may occur after the delivery date.
Receive proposal from client
Develop AI/ML model with client