Artificial Intelligence (AI)
- Data collection and preparation: Collection, cleaning, and preparation of large datasets for machine learning models.
- Supervised learning: Building and training models using labeled data, with the goal of predicting outcomes for new, unlabeled data.
- Unsupervised learning: Building and training models to identify patterns and relationships in data without any labeled examples.
- Deep learning: Using artificial neural networks to create models that can learn and make predictions from large and complex datasets.
- Natural language processing (NLP): Building models that can process and analyze human language, including speech recognition, sentiment analysis, and text classification.
- Computer vision: Building models that can interpret and analyze visual information, such as image and video recognition.
- Predictive analytics: Developing models to predict future outcomes based on historical data, such as sales forecasting or customer behavior prediction.
- Model evaluation and optimization: Testing and refining machine learning models to ensure they are accurate and efficient.
- Deployment and integration: Integrating machine learning models into existing systems, and deploying them to production environments.
- Consulting and training: Providing consultation on machine learning strategy, implementation, and management, as well as training on machine learning tools and techniques.