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Parallelization in Python and Machine Learning 1. What is Parallelization? Parallelization is the process of running multiple tasks simultaneously instead of sequentially, which reduces total computation time. Sequential execution: Tasks run one after another. Example: Evaluate 23 hyperparameter combinations one by one. Parallel execution: Tasks are distributed across multiple CPU cores and run simultaneously. Example: Evaluate 23 combinations at the same time. 2. Parallelization in Python Python provides several ways to run tasks in parallel: multiprocessing module: Creates separate processes that run on different CPU cores, avoiding the Global Interpreter Lock (GIL). concurrent.futures.ProcessPoolExecutor: High-level interface for asynchronous task execution. Example: from multiprocessing import Pool def task(x): # Simulate some computation return x*x if __name__ == "__main__": inputs = list(range(23)) # 23 ...