[MINOR][PYTHON][DOCS] Fix some typos in docstrings and messages

### What changes were proposed in this pull request?

This PR fixes some types in PySpark docstrings / error messages:

- `DefautParamsWritable` -> `DefaultParamsWritable`
- `paramter` -> `parameter`

### Why are the changes needed?

Typos.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Not tested.

Closes apache#34952 from zero323/TYPOS-PYTHON.

Authored-by: zero323 <[email protected]>
Signed-off-by: Hyukjin Kwon <[email protected]>

python/pyspark/ml/util.py

@@ -366,7 +366,7 @@ def write(self):
             return DefaultParamsWriter(self)
         else:
             raise TypeError(
-                "Cannot use DefautParamsWritable with type %s because it does not "
+                "Cannot use DefaultParamsWritable with type %s because it does not "
                 + " extend Params.",
                 type(self),
             )

python/pyspark/pandas/namespace.py

@@ -537,12 +537,12 @@ def read_delta(
     version : string, optional
         Specifies the table version (based on Delta's internal transaction version) to read from,
         using Delta's time travel feature. This sets Delta's 'versionAsOf' option. Note that
-        this paramter and `timestamp` paramter cannot be used together, otherwise it will raise a
+        this parameter and `timestamp` parameter cannot be used together, otherwise it will raise a
         `ValueError`.
     timestamp : string, optional
         Specifies the table version (based on timestamp) to read from,
         using Delta's time travel feature. This must be a valid date or timestamp string in Spark,
-        and sets Delta's 'timestampAsOf' option. Note that this paramter and `version` paramter
+        and sets Delta's 'timestampAsOf' option. Note that this parameter and `version` parameter
         cannot be used together, otherwise it will raise a `ValueError`.
     index_col : str or list of str, optional, default: None
         Index column of table in Spark.