Data collection and storage: Use databases like PostgreSQL, MySQL, or NoSQL solutions like MongoDB or Cassandra for data storage. Employ Apache Kafka or RabbitMQ for data streaming and real-time processing. Data preprocessing and transformation: Use libraries like Pandas, NumPy, and Dask for data manipulation and transformation in Python. Apply Apache Spark or Hadoop for big data processing and distributed computing. Machine learning frameworks and libraries: TensorFlow and Keras: Developed by Google, these open-source libraries provide a flexible and efficient platform for building and deploying ML models. PyTorch: Developed by Facebook, PyTorch offers a dynamic computation graph, making it suitable for research and rapid prototyping. Scikit-learn: A widely-used Python library with a broad range of ML algorithms, including classification, regression, and clustering. XGBoost and LightGBM: Gradient boosting libraries known for their high performance and scalability. Natural Language Pro...
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