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Lijala Shakya

@lijalashakya

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Computer Engineering student and data science enthusiast skilled in machine learning and deep learning.

Nepal

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What I'm looking for

I am seeking an internship or entry-level role where I can apply and expand my ML/deep learning skills, work on real-world projects, and learn from experienced engineers.

I am a Computer Engineering student and data science enthusiast with hands-on experience building ML and deep learning models using Python and its libraries.

I have developed projects including a job recommendation system using TF-IDF and cosine similarity, a CNN-based Alzheimer’s stage classifier with experiments using ViT and SVM, and an automated essay grader combining TF-IDF, linguistic features, and Ridge Regression.

I work with tools and libraries such as Scikit-Learn, Pandas, NumPy, Matplotlib, TensorFlow/Keras, ViT, CNNs, SVMs, and use Git/GitHub, VSCode and Colab for development and reproducibility.

I am currently seeking an internship or entry-level opportunity to apply my technical skills to real-world problems and to continue growing my practical expertise in machine learning and data science.

Experience

Work history, roles, and key accomplishments

Machine Learning Intern

Self Initiated

Built multiple ML projects including a job recommendation system using TF-IDF and cosine similarity, a CNN-based Alzheimer’s stage classifier, and an automated essay grader combining TF-IDF with Ridge regression to produce reproducible pipelines.

Python TensorFlow Keras scikit learn CNN TF IDF Cosine Similarity

Education

Degrees, certifications, and relevant coursework

Lalitpur Engineering College

Bachelor of Computer Engineering, Computer Engineering

2022 -

Activities and societies: Developed CNN-based Alzheimer's detection, job recommendation system, and other ML projects; uses Python, TensorFlow, Keras, Scikit-Learn, Git.

Pursuing a Bachelor's of Computer Engineering with coursework and projects focused on AI, CNNs, ViT, and machine learning applied to medical imaging and recommendation systems.