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SebastianCarvalhoSalazar/README.md

👋 Hi, I'm Sebastian Carvalho Salazar!

Machine Learning Engineer 🤖 | 🎧 Sound Engineer | 🧠 Data Scientist

Python MATLAB R C++ SQL Ruby JavaScript HTML5 CSS3 AWS Docker

TensorFlow Keras PyTorch Scikit-learn Hugging Face MLFlow Weights & Biases Prefect GitHub Actions FastAPI Flask JUCE Ruby on Rails

Natural Language Processing Computer Vision Transfer Learning Digital Signal Processing Fourier Transform Wavelet Transform Audio Processing Signal Filtering

🌍 Connect with me:

LinkedIn

🛠️ Key Skills and Technologies:

  • Machine Learning: Supervised/unsupervised models, hyperparameter tuning, GridSearchCV, SVM, GradientBoosting.
  • Deep Learning: Neural networks with TensorFlow, PyTorch.
  • Data Science: Data analysis, feature engineering, data pipelines with Python (Pandas, NumPy, Scikit-learn).
  • Deployment: ML models in production using Flask, Docker, APIs.
  • Languages: Python, R, C++, MATLAB, SQL.

💼 Professional Experience:

  • Credit Risk Models Developer: Led the successful implementation of predictive models for credit risk evaluation, including incurred and expected loss models, improving the company’s ability to manage and forecast financial risk.

  • Real-time Noise Measurement Algorithm Developer: Designed and implemented a real-time algorithm for noise level measurement, utilizing IoT technology, and achieving Class 1 sound level meter certification.

  • Web Scraping and Data Analysis Lead: Developed advanced scraping algorithms and utilized graph-based data visualization to enhance marketing decision-making and data completeness.

🌟 Featured Projects:

  • Spark-NLP-Bank-Complaints-Classification: This repository focuses on classifying customer complaints related to various banking products using Spark NLP. It includes natural language processing techniques, classification models, and advanced hyperparameter optimization.
  • Happiness Prediction: Predicting happiness using classification models and advanced hyperparameter optimization.
  • GraphInsight: GraphInsight is a recommendation system that uses a bipartite graph to link users and products based on their interactions. By calculating user similarities, it recommends products using Python libraries like Pandas and NetworkX, leveraging a synthetic dataset for experimentation.

📊 My GitHub Stats & 📈 Top Languages:

GitHub Stats Top Languages
GitHub Stats Top Languages

🎓 Certifications:

Pinned Loading

  1. 8BandEQ 8BandEQ Public

    Eight band equalizer developed in JUCE Framework 6.0.1.

    C++ 1

  2. AcousticSourceModeling AcousticSourceModeling Public

    In line array systems, it is assumed that all sources are equal to guarantee a correct performance in the sound radiation of these, however, in practice this does not happen, since some components …

    MATLAB 1 2

  3. KnowledgeTransferTensorflowJS KnowledgeTransferTensorflowJS Public

    In this example we use a pre-trained model of image classification and by doing knowledge transfer we train another model that can identify 5 specific types of objects.

    JavaScript

  4. Some-ML-Algorithms Some-ML-Algorithms Public

    Python

  5. GraphInsight GraphInsight Public

    Python

  6. Inferential-Statistics-with-R Inferential-Statistics-with-R Public

    R