I am an Electronic Instrumentation Engineer currently pursuing a Master's degree in Computing and Electronic Engineering. My expertise spans across Data Science, Machine Learning, and Artificial Intelligence, with a particular focus on digital signal processing (DSP) and emotion recognition from speech.

• Machine Learning: Solid foundations in various ML techniques including regression, classification, clustering, and deep learning models such as CNNs, RNNs, Transfer Learning & Fine Tuning.

• Digital Signal Processing: Extensive experience in DSP applications, particularly in voice processing and speech emotion recognition.

• Advanced Techniques: Proficiency in tools like Wav2vec2, eGeMAPS, HuBERT, Whisper for advanced speech recognition and processing.

• Big Data Technologies: Familiar with Apache Spark, Databricks and IBM Watson.

• Database Management: Experienced in managing databases with MongoDB, MySQL, and SQL.

• Computer Vision: Knowledgeable in using opencv, ML algorithms and YOLO for various applications. Skilled in using LabelStudio for Data Annotation

• Dashboards: Strong knowledge in Power BI and creating dashboards with Python.

• 📫 How to reach me:

• Knowledge Base:

  • Basic Concepts: Regression, Random Forests, SVM, K-means, KNN, ensemble methods, perceptrons, dimensionality reduction, MLP, CNN (1D, 2D, 3D), RNN, etc.
  • Advanced Topics: Transfer learning, active learning, ensemble methods, self-labeling, hybrid networks, non-linear models, GANs, autoencoders.

• Applications:

  • DSP (Imaging and Audio processing), speech emotion recognition, classification, resonant magnetic imaging (fMRI), regression, prediction, dashboards.

• Tools and Libraries:

  • Pytorch, TensorFlow, Scikit-learn, PIL, OpenCV, Dash, MATLAB and more.

• Tools and Libraries:
  • Extensive experience with dashboards, matplotlib, seaborn, plotly, folium, and more.
  • Strong knowledge in Power BI.

• Technologies and Tools: YOLO, Google DeepDream, LabelStudio, and other advanced computer vision techniques.