I'm a passionate Data Scientist and AI Engineer dedicated to developing AI solutions that address real-world problems. My journey in the field of Artificial Intelligence began with a fascination for Alexa and has since evolved through extensive learning and hands-on projects. I have a deep-seated love for AI and data, and I'm eager to collaborate on innovative projects that make a significant impact.

• Artificial Intelligence
• Machine Learning
• Data Science
• AI Engineering

I am currently focused on:

• Data Science and Artificial Intelligence Engineering
• Building AI modules that impact various areas of life
• Developing a startup that provides real-time data analytics solutions for SMEs

• To develop AI solutions to world problems using data
• To inspire data scientists to leverage Large Language Models (LLMs)
• To continue expanding my knowledge and expertise in AI and machine learning

• Email: [email protected]

• Python: Proficient in Python programming with a focus on AI and data science applications.
• PyTorch: Extensive experience with PyTorch for building and training neural networks.

• Algorithms: Linear Regression, Random Forest, Decision Trees, Clustering (K-means), RNNs, LSTMs, ANNs, CNNs.
• Libraries and Frameworks: Scikit-learn, PyTorch.
• Techniques: Data cleaning, preprocessing, visualization, exploratory data analysis (EDA).

• Jupyter Notebooks: For developing and presenting machine learning projects.
• Power BI: Crafting insightful dashboards and visualizations.
• GitHub: Version control and collaboration on projects.

• Statistics: Strong foundation in statistical analysis and data interpretation.
• Data Visualization: Proficient in using Matplotlib and other visualization tools to convey insights effectively.

• Teamwork: Experienced in collaborative environments, working on interdisciplinary teams.
• Documentation: Clear and modular code documentation practices for future reference.

• Description: Predicted solar yields using machine learning.
• Techniques Used: Linear Regression (sklearn), Deep Learning Linear Regression (PyTorch), LSTM (PyTorch).
• Highlights: Trend analysis, correlation analysis, preprocessing with Standard Scaler, and model training using ADAM optimizer and Mean Squared Error loss function.

• Description: Conducted exploratory data analysis on Grammy Awards data.
• Tools Used: Matplotlib, Pandas.
• Highlights: Analysis of artists' records, racial representation, nominations and awards, and genre-specific insights.

• Description: Grouped customers based on features such as marital status, education, number of children, and recency using K-means clustering.
• Techniques Used: Elbow Method, silhouette analysis.
• Highlights: Identified four unique customer clusters, providing actionable insights for targeted marketing strategies.

• Description: Developed a chatbot to support individuals with mental health concerns.
• Technologies Used: DialoGPT, natural language processing.

• Description: Created a system to recommend books based on user preferences.
• Techniques Used: Collaborative filtering, content-based filtering.

• Description: Built a chatbot to help introverted individuals engage in conversations.
• Technologies Used: Natural language processing, machine learning.

I began my journey into Machine Learning and AI two years ago, inspired by the potential of Alexa. Since then, I have attended boot camps and completed online courses from platforms like Udemy, Coursera, and Udacity, focusing on Python, Data Science, and Machine Learning. I have also explored various algorithms and frameworks, including:

• Daniel Bourke's YouTube course on PyTorch
• Kaggle for practical applications and references

• Mastered foundational algorithms: Linear Regression, Random Forest, Decision Trees.
• Delved into PyTorch framework and advanced models like NLP, Sequential Models, and Computer Vision.
• Applied knowledge in internships, working on projects such as food recognition models, sign language recognition, and sales prediction using machine learning.

Thank you for visiting my GitHub page! I'm always open to collaborations and discussions on AI and data science. Feel free to reach out to me via email.