1. Machine Learning Models: Supervised learning, unsupervised learning, reinforcement learning, deep learning, neural networks, decision trees, random forests, support vector machines (SVM), clustering algorithms, etc.
2. AI Techniques: Natural language processing (NLP), computer vision, generative adversarial networks (GANs), transfer learning, etc.
3. Visualization Tools: Tableau, Power BI, Matplotlib, Seaborn, Plotly, etc.
4. Databases: Snowflake, Teradata, SQL, NoSQL databases.
5. Programming Languages: Python (essential), R, SQL.
6. Python Libraries: TensorFlow, PyTorch, scikit-learn, pandas, NumPy, Keras, SciPy, etc.
7. Data Processing: ETL processes, data warehousing, data lakes.
8. Cloud Platforms: AWS, Azure, Google Cloud Platform.
