CSE Student • AI/ML Enthusiast

Building Intelligent Systems

Computer Science student interested in Artificial Intelligence and Machine Learning, creating innovative solutions that learn and adapt.

About Me

Passionate about creating elegant solutions to complex problems

Hello, I'm Aakash,

As a Computer Science Engineering student with a keen interest in Artificial Intelligence and Machine Learning, I'm dedicated to building intelligent systems that solve real-world problems.

My journey in AI/ML involves exploring deep learning architectures, natural language processing, agentic AI systems, and retrieval-augmented generation. I believe in the transformative power of AI when combined with ethical considerations and practical implementation.

When I'm not training models or analyzing data, you can find me reading research papers, participating in Kaggle competitions, or contributing to open-source ML projects.

Machine Learning

TensorFlow, Scikit-learn, Keras

Deep Learning

CNNs, RNNs, Transformers, GANs

Data Science

Pandas, NumPy, Matplotlib, Seaborn

Programming

Python, DSA, SQL

Databases

MySQL, PostgreSQL

Tools & Technologies

Git, Jupyter, Linux

Get to Know Me

👤

Full Name

Aakash Dandekar

📱

Phone

+91 8530095938

🎓

Education

B.Tech in Computer Science Engineering

🏫

University

PARUL INSTITUTE OF TECHNOLOGY., LIMDA, VAGHODIA 037

💼

Interests

Machine Learning • Deep Learning • Agentic AI • NLP • RAG

🛠️

Technical Skills

DSA Python • Databases • Python Programming • Machine Learning • Neural Network

Certifications

Professional certifications and verified achievements

Python

IBM

Issued 2026

Skills: Python (Programming Language)

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Supervised Machine Learning: Regression and Classification

DeepLearning.AI

Issued Jan 2026

Credential ID: N4SWFKRF1PDL

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Advanced Learning Algorithms

DeepLearning.AI

Issued Feb 2026

Credential ID: PYGCGJGDNPHF

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Featured Projects

A selection of projects that showcase my skills and passion

01

Image Classification System

A deep learning CNN model for multi-class image classification with 95% accuracy. Implemented data augmentation and transfer learning using ResNet50.

Key Features:
  • Transfer learning with ResNet50 architecture
  • Custom data augmentation pipeline
  • Real-time inference with optimized model
  • Confusion matrix and classification reports
Dataset: 50,000+ images across 10 categories
Performance: 95.2% accuracy, F1-score: 0.94
PyTorch CNN Computer Vision
02

Sentiment Analysis Tool

NLP-based sentiment analyzer using BERT for social media text. Deployed as a REST API with real-time prediction capabilities.

Key Features:
  • Fine-tuned BERT model on Twitter dataset
  • Multi-class sentiment classification (positive, negative, neutral)
  • RESTful API with Flask backend
  • Batch processing support for large datasets
Technologies: HuggingFace Transformers, Flask, Docker
Accuracy: 92.8% on test set
Transformers NLP Flask