Fix article

content/portfolio/what-is-machine-learning.md

@@ -68,9 +68,15 @@ Once the model type is defined, the next step is to delve into the full workflow
 
 A machine learning project generally follows these steps:
 
-1. **Data Preparation**\* Splitting data into training and testing sets.* Preprocessing: scaling, handling missing values, etc.
-2. **Model Training**\* Fitting the model on training data: `model.fit(X, y)`.* Optimising parameters and hyperparameters.
-3. **Prediction and Evaluation**\* Making predictions on unseen data: `model.predict(X)`.* Comparing predictions ($$\hat{y}$$) with actual values ($$y$$).
+1. **Data Preparation**
+   - Splitting data into training and testing sets.
+   - Preprocessing: scaling, handling missing values, etc.
+2. **Model Training**
+   - Fitting the model on training data: `model.fit(X, y)`.
+   - Optimising parameters and hyperparameters.
+3. **Prediction and Evaluation**
+   - Making predictions on unseen data: `model.predict(X)`.
+   - Comparing predictions ($$\hat{y}$$) with actual values ($$y$$).
 
 ![Modelization in Prog.png](/portfolio/ML/model.png)