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.
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Machine Learning: Solid foundations in various ML techniques including regression, classification, clustering, and deep learning models such as CNNs, RNNs, Transfer Learning & Fine Tuning.
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Digital Signal Processing: Extensive experience in DSP applications, particularly in voice processing and speech emotion recognition.
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Advanced Techniques: Proficiency in tools like Wav2vec2, eGeMAPS, HuBERT, Whisper for advanced speech recognition and processing.
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Big Data Technologies: Familiar with
Apache Spark,DatabricksandIBM Watson. -
Database Management: Experienced in managing databases with MongoDB, MySQL, and SQL.
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Computer Vision: Knowledgeable in using
opencv, ML algorithms and YOLO for various applications. Skilled in usingLabelStudiofor Data Annotation -
Dashboards: Strong knowledge in
Power BIand creating dashboards with Python. -
📫 How to reach me:
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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.
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Applications:
- DSP (Imaging and Audio processing), speech emotion recognition, classification, resonant magnetic imaging (fMRI), regression, prediction, dashboards.
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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.
- Extensive experience with dashboards,
- Technologies and Tools: YOLO, Google DeepDream, LabelStudio, and other advanced computer vision techniques.
