You will get a State of the Art AI driven application
Project details
Talal is known for his expertise in executing comprehensive projects spanning from Data Mining and ETL processes to the development and deployment of sophisticated Deep Learning models. With a profound understanding of the intricacies of Natural Language and Deep Learning research, Talal actively explores emerging trends and techniques in machine learning, deep learning, and reinforcement learning. His unique forte lies in implementing cutting-edge methodologies and algorithms for tasks such as natural language generation, comprehension, and the creation of intelligent autonomous multi-agent systems.
What sets Talal apart is his extensive experience in Multimodal AI, where he adeptly integrates diverse modalities like text, speech, image, and video to craft highly intelligent and natural systems. Moreover, his proficiency in NeuroSymbolic AI distinguishes him as he seamlessly combines symbolic and neural approaches, resulting in the development of more robust and interpretable systems. Throughout his career, Talal has successfully completed numerous courses and projects, showcasing his commitment to staying at the forefront of technological advancements in the field.
What sets Talal apart is his extensive experience in Multimodal AI, where he adeptly integrates diverse modalities like text, speech, image, and video to craft highly intelligent and natural systems. Moreover, his proficiency in NeuroSymbolic AI distinguishes him as he seamlessly combines symbolic and neural approaches, resulting in the development of more robust and interpretable systems. Throughout his career, Talal has successfully completed numerous courses and projects, showcasing his commitment to staying at the forefront of technological advancements in the field.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceAI Tools
Amazon SageMaker, Azure Machine Learning, BigDL, Chainer, Keras, MLflow, OpenCV, PyBrain, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$5,000
|
Standard
$20,000
|
Advanced
$50,000
|
|---|---|---|---|
| Delivery Time | 14 days | 30 days | 60 days |
Number of Revisions | 1 | 1 | 1 |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | ||
Model Documentation | - | ||
Ontology | - | - | |
Source Code | - | - | |
Taxonomy | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$2,000 - $5,000
Additional Revision
+$1,000
Real-time API Integration:
(+ 7 Days)
+$5,000
Extended Support and Maintenance
(+ 10 Days)
+$5,000
Advanced Analytics Dashboard
(+ 7 Days)
+$5,000Frequently asked questions
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WB
William B.
Apr 14, 2025
Mr. William - CTO / Chief DS & AI Development and Consultancy Contract
very good
About Talal
Data Scientist | Generative AI Expert | Deep Learning Engineer
Wah Cantt, Pakistan - 5:59 am local time
Skills:
Data Engineering
Data Science
Statistics and Probability
Machine Learning
Deep Learning
Reinforcement Learning
Knowledge Graphs
Multi-Modal AI
NeuroSymbolic AI
Computational Linguistics
Full Stack Web Development
Tools and Technologies:
Programming Languages:
Python: Core language for data science and machine learning.
R: Statistical programming language for data analysis.
C++: Core language for Financial Trading Systems specially where low latency is required.
Javascript: Front-end development for dynamic and interactive user interfaces.
Machine Learning Frameworks:
PyTorch: Deep learning framework for research and development.
scikit-learn (sklearn): Machine learning library for classical algorithms.
Keras: High-level neural networks API.
Numerical and Scientific Computing:
NumPy: Fundamental package for scientific computing.
Pandas: Data manipulation and analysis library.
SciPy: Library for scientific and technical computing.
Web Scraping and Automation:
Requests: HTTP library for making web requests.
Beautiful Soup: Library for pulling data out of HTML and XML files.
Selenium: Browser automation tool.
Scrapy: Open-source and collaborative web crawling framework.
Data Visualization:
Matplotlib: Plotting library for creating visualizations.
Seaborn: Statistical data visualization based on Matplotlib.
Plotly: Data visualization library.
Natural Language Processing (NLP):
NLTK (Natural Language Toolkit): Library for working with human language data.
spaCy: Advanced NLP library for various tasks.
Gensim: Library for topic modeling and document similarity analysis.
Game Development and Reinforcement Learning:
Pygame: Set of Python modules designed for writing video games.
OpenAI Gym: Toolkit for developing and comparing reinforcement learning algorithms.
Computer Vision and Image Processing:
OpenCV: Library for computer vision and image processing.
Web Development and APIs:
Django:High-level Python web framework for rapid development of secure and maintainable websites.
Flask: Web framework for building web applications in Python.
FastAPI: Fast (high-performance), web framework for building APIs with Python 3.7+.
React: Front-end development for dynamic and interactive user interfaces.
Database and Storage:
SQL (Structured Query Language): Language for managing relational databases.
NoSQL: Databases like MongoDB for flexible and scalable data storage.
Big Data Databases: Databases like HBase and Google Bigtable, handle large volumes of distributed data.
Vector Databases: Databases, such as FaunaDB and InfluxDB, excel in managing and querying vectorized data.
Graph Databases: Databases, like Neo4j and Amazon Neptune, specialize in storing and querying graph-structured data.
Big Data Processing:
Apache Hadoop: Framework for distributed storage and processing of large data sets.
Apache Spark: Open-source distributed computing system for big data processing.
Containerization and Deployment:
Docker: Platform for automating the deployment of applications.
Version Control:
Git: Distributed version control system for tracking changes in source code.
Continuous Integration/Continuous Deployment (CI/CD):
Jenkins: Automation server for building, testing, and deploying code.
Cloud Computing and AI Services:
AWS (Amazon Web Services): Cloud computing services offering a wide range of functionalities.
Azure Machine Learning: Cloud-based platform for building, deploying, and managing machine learning models.
Testing:
PyTest: Testing framework for Python code.
Virtual Environments and Dependency Management:
Virtualenv: Tool for creating isolated Python environments.
Conda: Open-source package management and environment management system.
Other Libraries and Technologies:
Requests: Library for handling HTTP requests.
PyBrain: Library for neural networks, machine learning, and optimization.
Steps for completing your project
After purchasing the project, send requirements so Talal can start the project.
Delivery time starts when Talal receives requirements from you.
Talal works on your project following the steps below.
Revisions may occur after the delivery date.
Project Kickoff Meeting and Requirement Analysis:
- Discussion of the project's objectives, scope, and specific data science requirements. -Detailed analysis of the data requirements. Identification of data sources, formats, and any specific preprocessing steps needed.
Data Collection and Exploration:
Gathering relevant datasets and exploring their characteristics. This step includes an initial assessment of data quality and potential issues.