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Data requirements gathering and client engagement.
- Being able to engage with the client to understand their pain points and requirements.
- Communicate solutions/propositions effectively back to client/team
Data analysis and pre-processing
- Collect and clean large datasets for machine learning projects.
- Explore data to identify patterns, anomalies, and potential insights.
- Pre-process data, including feature engineering and normalization.
Algorithm Development
- Design, develop, and implement machine learning algorithms.
- Experiment with various machine learning models and techniques.
- Optimize algorithms for accuracy, efficiency, and scalability.
Model Training and Evaluation
- Train machine learning models using collected data.
- Perform testing to validate models
- Evaluate model performance using appropriate metrics and techniques.
- Fine-tune models to improve predictive accuracy.
Data Visualization
- Create informative data visualizations to communicate insights effectively
- Familiarity with visualization libraries like Matplotlib, Seaborn, or data visualization software’s like Tableau, PowerBl