update homepage, h2 fix

Author: mkumar73

docs/homepage.md

@@ -89,7 +89,8 @@ Mambular simplifies data preprocessing with a range of tools designed for easy t
 
 
 <h2> Fit a Model </h2>
-Fitting a model in mambular is as simple as it gets. All models in mambular are sklearn BaseEstimators. Thus the `.fit` method is implemented for all of them. Additionally, this allows for using all other sklearn inherent methods such as their built in hyperparameter optimization tools.
+
+Fitting a model in mambular is as simple as it gets. All models in mambular are sklearn BaseEstimators. Thus, the `fit` method is implemented for all of them. Additionally, this allows for using all other sklearn inherent methods such as their built in hyperparameter optimization tools.
 
 ```python
 from mambular.models import MambularClassifier
@@ -101,12 +102,12 @@ model = MambularClassifier(
     n_bins=50,
     d_conv=8
 )
-
 # X can be a dataframe or something that can be easily transformed into a pd.DataFrame as a np.array
 model.fit(X, y, max_epochs=150, lr=1e-04)
 ```
 
 Predictions are also easily obtained:
+
 ```python
 # simple predictions
 preds = model.predict(X)
@@ -116,6 +117,7 @@ preds = model.predict_proba(X)
 ```
 
 <h3> Hyperparameter Optimization</h3>
+
 Since all of the models are sklearn base estimators, you can use the built-in hyperparameter optimizatino from sklearn.
 
 ```python
@@ -145,7 +147,11 @@ random_search.fit(X, y, **fit_params)
 print("Best Parameters:", random_search.best_params_)
 print("Best Score:", random_search.best_score_)
 ```
-Note, that using this, you can also optimize the preprocessing. Just use the prefix ``prepro__`` when specifying the preprocessor arguments you want to optimize:
+
+
+**Note:** that using this, you can also optimize the preprocessing. Just use the prefix ``prepro__`` when specifying the preprocessor arguments you want to optimize:
+
+
 ```python
 param_dist = {
     'd_model': randint(32, 128),  
@@ -156,7 +162,6 @@ param_dist = {
 
 ```
 
-
 Since we have early stopping integrated and return the best model with respect to the validation loss, setting max_epochs to a large number is sensible.
 
 
@@ -300,6 +305,7 @@ Here's how you can implement a custom model with Mambular:
    ```
 
 # Custom Training
+
 If you prefer to setup custom training, preprocessing and evaluation, you can simply use the `mambular.base_models`.
 Just be careful that all basemodels expect lists of features as inputs. More precisely as list for numerical features and a list for categorical features. A custom training loop, with random data could look like this.