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Data Analysis Portfolio

🧭 Table of Contents

Data Science and Data Analysis

Traditional Machine Learning

  • Core Models: Explore the development and tuning of ten essential machine learning models, designed to solve a variety of data problems. 10 ML Models
  • Clustering and University Projects: Engage with complex clustering techniques applied in an academic setting. University Clustering Exercise

Natural Language Processing (NLP)

  • Comprehensive Course: Embark on a structured journey into NLP with a curated course that covers both theory and application. NLP Course
  • Transformers: Leverage PyTorch to implement cutting-edge transformer models that drive modern NLP solutions. Transformer Implementations
  • Academic Projects: Delve into project work that integrates NLP applications within an academic framework. ML Course Project

Computer Vision

  • Dedicated Course: Gain critical insights into computer vision technologies and their applications across various sectors. CV Course
  • Cross-Domain Applications: Understand the similarities and applications of NLP transformers in vision-based models. See NLP Section

PyTorch

  • Official Tutorials and Beyond: Explore official PyTorch tutorials along with my personal projects that extend its capabilities in real-world scenarios. PyTorch Tutorials
  • Applications in AI: See practical applications in areas such as reinforcement learning and NLP. Reinforcement Learning, NLP

Tensorflow

  • Structured Learning: Navigate through an extensive course on Tensorflow, illustrating its use in various machine learning paradigms. Tensorflow Course, Kaggle Projects
  • Cross-Disciplinary Knowledge: Apply Tensorflow techniques in computer vision and natural language processing projects. CV Course, NLP Course

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Data Analysis, Machine Learning, Deep Learning. Projects, papers and codes.

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