This repository contains projects and code implementations from the "Machine Learning" course, which covered various aspects of machine learning and data analysis. Here are the key highlights:
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Data Collection:
- Gathered a diverse voice dataset comprising 16,000 samples for model training.
- Demonstrated proficiency in data collection for machine learning tasks.
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Data Preprocessing:
- Cleaned the collected data by removing corrupted samples.
- Ensured consistent voice length to prepare the data for model training.
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Feature Extraction with Librosa:
- Utilized the Librosa library to extract relevant acoustic features from voice samples.
- Demonstrated the importance of feature engineering in machine learning.
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Emotion and Gender Recognition with Machine Learning:
- Implemented various machine learning models, including SVM, MLP, KNN, and Clustering.
- Applied these models to accurately identify both emotion and gender from voice samples.
- Optimized recognition performance through model selection and tuning.
These projects highlight your practical experience in data collection, data preprocessing, feature engineering, and the application of machine learning techniques for emotion and gender recognition from voice data.
Feel free to explore the individual project folders within this repository for more details and code implementations.