Welcome to my GitHub! I'm Vijay Takbhate, a passionate Machine Learning and AI enthusiast with a strong foundation in mathematics and its real-world applications.
- 💡 I specialize in turning data into insights and thrive on solving complex problems with innovative solutions.
- 🎓 Certified MLOps Expert, with expertise in deploying and managing machine learning models in production environments.
- 🚀 Passionate about cutting-edge topics like Large Language Models (LLMs), fine-tuning, and Retrieval-Augmented Generation (RAG).
- Machine Learning & AI: Developing algorithms, models, and systems to solve real-world challenges using data.
- MLOps: Automating and optimizing the deployment of machine learning models in production.
- LLMs & Fine-Tuning: Working with large-scale models to create intelligent, adaptable systems.
- RAG: Exploring innovative approaches to enhance model performance through Retrieval-Augmented Generation techniques.
- Built an advanced ETL pipeline with PySpark and SQL for processing sentiment data.
- Conducted sentiment analysis using NLP techniques like TF-IDF and optimized the model through hyperparameter tuning.
- Managed data and feedback through Google Cloud Storage and MySQL.
- Deployed as a Dockerized web application on Render with monitoring via MLFlow.
- Designed a state-of-the-art chatbot powered by the NVIDIA API, offering functionalities such as:
- 📝 Grammar correction
- 🔄 Paraphrasing
- 📜 Plagiarism checking
- 📚 Content summarization
- Built with Flask and integrated with a cloud database for efficient deployment.
Description:
In this project, I explored healthy fast foods and clustered them into three groups based on calorie count. Using these clusters, I identified the healthiest fast food category. This will help people avoid harmful fast foods.
Achievements: Bronze Medal 🏅 | 4,604 Views 📊
Description:
This project focuses on image processing and the construction of a CNN model to predict cancer with 98% accuracy. I also analyzed the model’s performance metrics.
Achievements: Bronze Medal 🏅 | 1,621 Views 📊
Description:
I trained a CNN model using 17,000 X-ray images to build a model for pneumonia detection. The project includes a website for easy interaction with the model.
Achievements: Bronze Medal 🏅 | 2,000 Views 📊
Description:
This NLP project covers the entire process from EDA, text processing, regex operations, TF-IDF, and BOW to model training.
Achievements: Bronze Medal 🏅 | 2,710 Views 📊
Description:
This project involves handling an imbalanced dataset for activity prediction. I explored techniques like undersampling, oversampling, and synthetic minorities. However, due to the low data for labels like stair descending and stair ascending, I avoided these techniques to prevent data loss and overfitting. The model is suitable for elder activity tracking, potentially deployable on hardware like Raspberry Pi.
Suggestions: Try the imbalance handling techniques mentioned in my notebook and share your results in the comments.
Achievements: Bronze Medal 🏅 | 680 Views 📊
Preview:
Learn the essentials of Docker, from building Dockerfiles to deploying Flask applications in a containerized environment.
Preview:
Discover the significance of statistical inference, including parametric inference and hypothesis testing, using real-world examples like the COVID-19 pandemic.
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Dive into the mathematical foundations of machine learning, with a focus on matrix operations and their pivotal role in ML algorithms.
Here is the updated section in the requested style:
📧 Email: [email protected]
🌍 GitHub: My Repositories
💼 LinkedIn: My Profile
📊 Kaggle: My Work
🎥 YouTube: Deep Neural Channel
⭐️ Explore my repositories and let's collaborate on impactful projects!