Learning & Certification
Continuous learning journey in AI, ML, and data science — each certification builds expertise for real-world applications.
Professional Certifications
Data, Data Everywhere
Google • Sep 2023
Course Overview
This course provided a strong foundation in data analytics concepts and practices. I learned how data is collected, structured, and used to drive decision-making in organizations. It covered key topics like data ecosystems, roles of data professionals, and the basics of using spreadsheets and SQL for analysis.
Relevance to My Work
Built my initial understanding of how raw data transforms into insights, which later supported my transition into machine learning and AI.
AI (Machine Learning & Deep Learning)
NAVTTC • Dec 2024
Course Overview
An advanced program focused on machine learning algorithms, deep learning architectures, and hands-on implementation. Topics included supervised/unsupervised learning, neural networks, CNNs, RNNs, and practical projects using Python frameworks like TensorFlow and PyTorch.
Relevance to My Work
Strengthened my ability to design, train, and evaluate ML/DL models, which I applied directly in projects like speech disorder detection and MindMate.
Introduction to Generative AI
DeepLearning.AI • Jan 2025
Course Overview
This certification introduced the core concepts behind generative AI, including how models like GPT and diffusion systems are trained and applied. It also explained prompt engineering, use cases for LLMs, and ethical considerations around AI deployment.
Relevance to My Work
Gave me the conceptual foundation to start building agentic applications with LLMs, such as BookMate (RAG-based document explorer) and multi-agent systems in MindMate.
Continuous Learning Philosophy
Each certification represents more than just a credential — it's a commitment to staying current with rapidly evolving technology. I believe in applying theoretical knowledge through hands-on projects, ensuring that every concept learned translates into practical skills that can solve real-world problems in AI and machine learning.
My learning approach focuses on understanding the underlying principles, implementing solutions through code, and connecting new knowledge to real-world applications. This philosophy has guided my journey from data analytics fundamentals to advanced AI and machine learning concepts.
Future Learning Goals
I'm committed to continuous learning and staying updated with the latest developments in AI and machine learning. My upcoming learning goals include:
- 🎯Advanced MLOps and model deployment strategies
- 🔬Research methodologies and academic paper analysis
- 🌐Large-scale distributed systems and cloud architecture
- 🤝Leadership and team management in technical environments