README.md

Neemias Buceli Data Science Portfolio

Context

Welcome to my Data Science Portfolio. Here, I showcase the insights and skills I have acquired throughout my journey in the field. You’ll find a collection of case studies, research papers, and code that illustrate my work and expertise.

Note: I regularly update this repository with new projects and links to additional repositories for more complex projects that exceed the scope of this one. Stay tuned for more exciting updates!

Table of Contents

• Math & Stat;
• Pogramming Skills;
• Machine Learning;
• Deep Learning.

Math & Stat

• Fundamental of Statistic;
  • Population vs Sample;
  • Mean;
  • Variance;
  • Standard Deviation (STD);
  • Correlation;
  • Covariance;
  • Probability Distribution Functions;
  • Bayes Theorem.
• Bootstraping;
• Expectation Algorithm;

Programming Skills

• Code Challenge;
• knapsack-problem-using-dp-grasp-tabu;
  • Dynamic programming;
  • Grasp Heuristic;
  • TABU Search.

Machine Leaning

• Machine Leaning Algorithms - Tutorial;
• Case Studies: Data Science Projects;
• Forecasting Data Analysis;
• Pattern Recognition:
  • Linear Algebra - Singular Value Decomposition;
    • Vector Calculus;
    • Probability and Distribution;
    • Optimization and Convex Optimization.
    • Machine Learning Algorithm:
      • Linear:
        • Perceptron;
        • Adaline;
        • Logistic Regression.
      • Non Linear:
        • SVM;
        • Decision Tree;
        • Random Forest;
        • Adaboost;
        • Gradient Boosting;
        • XGBoost.
• Predict wich new visitors will come back and purchase using the Google Merchandise Store dataset.

Deep Learning

• Regression in Convolutional Neural Network for Applied to Plant Leaf Count;
• Music Gener classification using MLFlow for tracking metrics in azure cloud
• MiniGPT4 for image caption generation;
• Developing a nano GPT-2 from scratch using PyTorch and training using the Fineweb datase.