Fix numbering

exercises/2/2.md

@@ -78,11 +78,11 @@ Create a new branch titled `knn_classifier` from the `main branch`. This is the
    ```
 ---
 2. **Set up the tests**:
-- Inside `tests/test.py` file implement functions called `test_distance`, `majority_vote`. The `test_distance` function should test the [distance properties](https://en.wikipedia.org/wiki/Euclidean_distance#Properties), while `majority_vote` should test that the algorithm implemented is correct (for example given `[1, 0, 0, 0]` the algorithm should return `0`).
-- Inside `tests/test.py` check the constructor of `kNN` (valid types).
+   - Inside `tests/test.py` file implement functions called `test_distance`, `majority_vote`. The `test_distance` function should test the [distance properties](https://en.wikipedia.org/wiki/Euclidean_distance#Properties), while `majority_vote` should test that the algorithm implemented is correct (for example given `[1, 0, 0, 0]` the algorithm should return `0`).
+   - Inside `tests/test.py` check the constructor of `kNN` (valid types).
 
-2. **Set up experiments**:
-- Inside `src/utils.py` add the following function use to read `yaml` files:
+3. **Set up experiments**:
+   - Inside `src/utils.py` add the following function use to read `yaml` files:
    ```python
    def read_config(file):
       filepath = os.path.abspath(f'{file}.yaml')
@@ -92,7 +92,7 @@ Create a new branch titled `knn_classifier` from the `main branch`. This is the
    ```
 ---
 
-- Inside `shell/submit.sh` file write the following line of code, which downloads the [Ionosphere](https://archive.ics.uci.edu/dataset/52/ionosphere) dataset and put it a directory called `./data`. Explore the dataset and in `src/utils.py` create a function named `read_file` which reads the dataset file and returns the features and labels as separate lists.
+    - Inside `shell/submit.sh` file write the following line of code, which downloads the [Ionosphere](https://archive.ics.uci.edu/dataset/52/ionosphere) dataset and put it a directory called `./data`. Explore the dataset and in `src/utils.py` create a function named `read_file` which reads the dataset file and returns the features and labels as separate lists.
    ```bash
    URL="https://archive.ics.uci.edu/static/public/52/ionosphere.zip"
    DEST_DIR="data"