- Data Science
- Machine Learning: Supervised and unsupervised learning, neural networks, and model evaluation.
- Deep Learning: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformers.
- Natural Language Processing (NLP): Sentiment analysis, text generation, and language models.
- Computer Vision: Image recognition, object detection, and generative models.
- Reinforcement Learning: Markov decision processes, Q-learning, and policy optimization.
- Programming Languages: Python, R
- Libraries: TensorFlow, PyTorch, Keras, scikit-learn
- Development Environments: Jupyter, Google Colab
- Version Control: Git, GitHub
