4. Sharpening Your Machine Learning Skills: A Guide to Train-Test Splitting with Python Arrays
   Purpose:In machine learning, splitting a dataset is crucial for training and evaluating models.The training set is used to "teach" the model by fitting it to the data's patterns
5. Pinpoint Python Performance Bottlenecks: Mastering Profiling Techniques
   Profiling is a technique used to identify and analyze the performance bottlenecks (slow parts) within your Python code. It helps you pinpoint which sections take the most time to execute
6. Dynamic Learning Rate Adjustment in PyTorch: Optimizing Your Deep Learning Models
   Understanding Learning Rate:The learning rate is a crucial hyperparameter in deep learning that controls how much the model's weights are updated during training
7. Keeping Your Python Code Clean: When Should Imports Be at the Top?
   Benefits of Placing Imports at the Top:Clarity: It provides a clear overview of all dependencies upfront, making the code easier to understand and maintain