Current Location: Kerman Province, Iran
- Data Science (Entry-level)
- AI Project Management (Entry-level)
- AI Research and Development (Entry-level)
- ...
- Open to remote work
- Not willing to relocate
🎓 I am pursuing a Master's in Electrical Engineering specializing in Telecommunications-System at the Shahid Bahonar University of Kerman. My academic focus includes various facets of artificial intelligence, including Machine Learning, Deep Learning, and Data Analysis. 💡 I am eager to apply my skills and knowledge in these areas to contribute effectively to AI-related roles.
- Organization
AI.Group- Region
Kerman, Kerman Province, Iran- Role
AI Project Manager2024-present
- Organization
IDPL-Lab- Region
Kerman, Kerman Province, Iran- Role
AI Research & Development (R&D)2021-2023
- Project scenario
A Data Science Coding Competition project that uses a real-world dataset to develop a prediction or classification model. Participants approached the problem using Python and Jupyter Notebook, focusing on data manipulation, feature engineering, and model evaluation. The competition allowed students to practice their problem-solving and critical thinking skills and gain experience with machine learning algorithms and data visualization techniques.
- Skills demonstrated
Data Science
Data Analysis
Python Programming
Machine Learning
- Tools used
Python
- Summary
Optimized data for balance, removed redundancy and enhanced predictive power using advanced techniques. Achieved project objectives seamlessly.
- Solution
In this project, I addressed data imbalances by configuring predictors to 'balanced' mode. Identified and removed redundant features using Gradient Boosting Classifier for feature selection. Applied One-Hot encoding to convert nominal to numerical data. Achieved optimal model performance through Hyperparameter tuning using Grid Search. Overall, I successfully streamlined the dataset and achieved project objectives.
- Approach
In carrying out the research, I began with extensive data exploration, resolving imbalances by configuring 'balanced' predictors. Using the Gradient Boosting Classifier, redundant features were identified and eliminated, nominal data was encoded with One-Hot, and feature selection was performed. We refined the model using hyperparameter tuning with grid search to ensure optimal performance and project completion.
- Project links
- Project scenario
You're a data analyst at a marketing firm that promotes brands on social media. Your team wants you to use Python to extract tweets based on specific categories (health, family, food, etc.), clean and analyze the data, and create visualizations. They will use your analysis to help clients improve their social media performance. This insight will allow the firm to deliver tweets on time and within budget, leading to faster results.
- Skills demonstrated
Seaborn
Data Analysis
Python
Programming
Pandas
Data Visualization
- Tools used
Python
- Summary
Taking on "Clean and Analyze Social Media Usage Data with Python," I described, displayed, and analyzed data. Python's "Data Visualization" improved my insights and honed my abilities for future assignments.
- Solution
Using Python to analyze social media data, I computed average likes and shares over time, uncovered trends, and grouped data by country to reveal engagement patterns. I used a correlation matrix to investigate relationships between likes and shares, demonstrating my ability to extract useful insights.
- Approach
To understand the dataset, I started the project by writing a descriptive "Data Description." Moving on to "Data Information," I established a solid foundation for future analysis. "Data Visualization" in Python improved clarity. With a well-prepared dataset, the "Data Analysis" step revealed significant patterns. This process demonstrated the successful use of Python for modification, visualization, and analysis in completing the "Clean and Analyze Social Media Usage Data with Python" project.
- Project links
Masters Student:
Electrical Engineering (Telecommunication-System)
@Shahid Bahonar University of Kerman
2020 - Present
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Bachelor's Degree:
Electrical Engineering (Telecommunication-System)
@Shahid Bahonar University of Kerman
2016 - 2020
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Other Educations:
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Note: For viewing the education details click on each image.
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Note: For viewing the specialization details click on each image.
Generative AI for Everyone | Coursera - DeepLearning.AI
Convolutional Neural Networks | Coursera - DeepLearning.AI
Introduction to Machine Learning in Production | Coursera - DeepLearning.AI
AI Capstone Project with Deep Learning | Coursera - IBM
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization | Coursera - DeepLearning.AI
Deep Neural Networks with PyTorch | Coursera - IBM
Advanced Learning Algorithms | Coursera - DeepLearning.AI, Stanford University
Supervised Machine Learning: Regression and Classification | Coursera - DeepLearning.AI, Stanford University
Building Deep Learning Models with TensorFlow | Coursera - IBM
Unsupervised Learning, Recommenders, Reinforcement Learning | Coursera - DeepLearning.AI, Stanford University
Structuring Machine Learning Projects | Coursera - DeepLearning.AI
Machine Learning | Coursera - Stanford University
Neural Networks and Deep Learning | Coursera - DeepLearning.AI
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This cybersecurity portfolio demonstrates expertise by auditing a fictional company's controls, risks, and compliance. The comprehensive report details the security posture through an asset inventory, risk assessment, control types and purposes, and a compliance checklist. It offers recommendations to mitigate identified risks and gaps. Conducting audits to identify and remediate vulnerabilities showcases cybersecurity skills.
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View the full document of this cybersecurity Portfolio here .
Convolutional Neural Networks (CNNs)
Artificial Neural Network
Transformers
Logistic Regression
Linear Regression
Decision Trees
Recommender Systems
Facial Recognition System
Object Detection and Segmentation
Concept Drift
ML Deployment Challenges
Mathematical Optimization
Tree Ensembles
Xgboost
Anomaly Detection
Reinforcement Learning
Collaborative Filtering
Multi-Task Learning
...
Recurrent Neural Networks (RNNs)
Transformers
Gated Recurrent Unit (GRU)
Long Short Term Memory (LSTM)
Attention Models
Machine Translation
Sentiment Analysis
Word Embeddings
Vector Space Models
...
Model Selection
Data Analysis
Data Visualization
Predictive Modelling
Data Science
R Markdown
...
Transformers
Image-to-Image Translation
StyleGANs
Large Language Models (LLMs)
AI Strategy
Generative AI Tools
AI Productivity
Pix2Pix
Privacy Preservation
...
Tensorflow
PyTorch
Pandas
Numpy
R Programming
Distributed Version Control Systems (DVCS)
Git
GitHub
...
Project Scoping and Design
Project Management
Change Management
Strategic Thinking
Organizational Culture
Career Development
...
Information Security (INFOSEC)
NIST Risk Management Framework (RMF)
Security Audits
NIST Cybersecurity Framework (CSF)
Incident Response Playbooks
Ethics in Cybersecurity
Historical Attacks
...
HTML
CSS
Javascript
Bootstrap
React