Hello! I am Goshika Uddav, a passionate B.Tech student at Ellenki College of Engineering and Technology, with a strong foundation in computer science, problem-solving, and emerging technologies. I am highly motivated to explore the fields of AI, Data Engineering, and IoT, continuously upskilling myself through hands-on experience and certifications.
I have completed multiple certifications, including:
✅ Data Analytics and Visualization Job Simulation – Accenture
🌟 AI Data Engineer Certificate Programme – NSDC Academy
✅ IoT Network Specialist – Retence Founantach Sk41ling Academy
🌟 Azure Machine Learning Fine-Tuning – Microsoft
With a keen interest in coding, data analytics, and AI, I have solved 50+ complex problems in Python and SQL, demonstrating my analytical and problem-solving abilities. My experience includes working on team projects, PC hardware troubleshooting, and software development. I thrive in fast-paced, innovative environments where I can apply my logical thinking, creativity, and technical skills to real-world problems. Beyond tech, I enjoy coding, music, and continuous learning. Let’s connect and innovate together! 🚀
• AI Foundation Certificate ▶
• Advanced AI Techniques ▶
• Java Programming ▶
• SQL Mastery ▶
• Data Analytics ▶
Voice Controlled AI
LLM-powered chatbot
AI in Industry
Frontend + AI
Tech Stack: Python, pyttsx3, SpeechRecognition, OpenAI
Description: A personal assistant that listens to voice commands, answers questions, and can automate desktop tasks.
Modern Use: Voice-driven task automation in smart homes and enterprise apps
Solves: Reduces manual input; ideal for accessibility and multitasking.
Trend Tie-In: Taps into Conversational AI and Accessibility Tech.
Tech Stack: HTML, JavaScript, OpenAI GPT
Description: A large language model chatbot with intelligent response capability and memory features.
Modern Use: Customer service, learning assistants, HR bots
Solves: Cuts response time, improves scalability for support teams
Trend Tie-In: Leverages Generative AI and LLM Integration in web apps.
Github Link:https://github.com/UddavGoshika/Prediction-Failure-ML-Model
Tech Stack: Python, scikit-learn, pandas, NumPy
Description: Trained ML model to detect real-time failures in an industrial pipeline.
Modern Use:Real-time anomaly detection in industrial setups
Solves: Prevents downtime, predicts machine failures before they happen
Trend Tie-In: Core to Industry 4.0, Predictive Maintenance, and AI in Manufacturing.
Tech Stack: HTML, CSS, JS, AI APIs
Description: Designed intelligent UI interfaces that dynamically interact with user behavior and AI predictions.
Modern Use: Seamless user experiences with AI personalization (chat, recommenders)
Solves: Bridges static websites with dynamic, smart features
Trend Tie-In: Focuses on AI-Powered UI/UX, Web 3.0, and Serverless Architecture
