glossaries

Author: kavaivaleri

docs/machine-learning-zoomcamp/module-01-introduction/crisp-dm.md

@@ -146,4 +146,4 @@ The CRISP-DM methodology provides a structured approach to machine learning proj
 
 The process is iterative, with each cycle building on lessons from previous iterations. Starting simple and gradually increasing complexity allows for faster delivery of value while maintaining a path for continuous improvement.
 
-In the next lesson, we'll dive deeper into the modeling step to explore how to select and evaluate different machine learning models.
+In the next lesson, we'll dive deeper into the modeling step to explore how to select and evaluate different machine learning models.

docs/machine-learning-zoomcamp/module-01-introduction/introduction-to-numpy.md

@@ -174,4 +174,4 @@ np.std(a)  # Standard deviation
 
 These operations work on both 1D and 2D arrays.
 
-NumPy offers many more functions for operations like finding minimums per row, sorting arrays, and matrix operations which will be covered in the linear algebra section.
+NumPy offers many more functions for operations like finding minimums per row, sorting arrays, and matrix operations which will be covered in the linear algebra section.

docs/machine-learning-zoomcamp/module-01-introduction/introduction-to-pandas.md

@@ -238,4 +238,4 @@ numpy_array = df['msrp'].values
 records = df.to_dict(orient='records')
 ```
 
-This format is useful for saving data or passing it to other systems that expect Python dictionaries.
+This format is useful for saving data or passing it to other systems that expect Python dictionaries.