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Always Coding
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Always Coding

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ansegura7/README.md

Hi there 👋

Hi, I’m Andrés Segura-Tinoco, a Senior Data Scientist with nearly two decades of experience designing and delivering AI and data-driven solutions for complex industrial environments, particularly in the Energy and Oil & Gas sectors.

I specialize in building scalable, end-to-end AI solutions integrating Machine Learning, NLP, Generative AI, advanced analytics, and software engineering, with a focus on reliable, practical solutions that deliver measurable real-world impact.

📚 My areas of interest include:

  • Artificial Intelligence and Machine Learning
  • Generative AI and Large Language Models
  • Natural Language Processing
  • Explainable AI (XAI)
  • Data-driven optimization and intelligent systems

You can find more information about my research and publications on my Google Scholar profile.

💻 My main programming languages and technologies include:

  • Python 🐍
  • SQL
  • Java
  • C# / .NET
  • JavaScript
  • R

📧 Feel free to contact me on Twitter (@SeguraAndres7) or LinkedIn.

E-Mail ansegura7

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  1. Algorithms Algorithms Public

    Free hands-on course with the implementation (in Python) and description of several computational, mathematical and statistical algorithms.

    HTML 134 22

  2. NLP NLP Public

    Free hands-on course with the implementation (in Python) and description of several Natural Language Processing (NLP) algorithms and techniques, on several modern platforms and libraries.

    HTML 81 16

  3. MachineLearning MachineLearning Public

    Practical course, which starting from Data Science offers examples (with Python code) and explanation (in Twitter threads) on concepts and techniques of Machine Learning, Deep Learning and NLP.

    Jupyter Notebook 76 12

  4. TwitterAnalytics TwitterAnalytics Public

    Web Mining project in which Descriptive Statistics and NLP techniques are used to analyze the behavior of a Twitter account and the content of their respective tweets.

    HTML 9 4

  5. RS_Surprise RS_Surprise Public

    Project with examples of different recommender systems created with the Surprise framework. Different algorithms (with a collaborative filtering approach) are explored, such as KNN or SVD.

    HTML 9 1

  6. RS_CF_LastFm RS_CF_LastFm Public

    Recommender systems with collaborative filtering created with Apache Mahout framework. The system uses a Music Recommendation dataset for research purposes as input, but you can train it and predic…

    Java 5 3