diff --git a/content/portfolio/what-is-machine-learning.md b/content/portfolio/what-is-machine-learning.md
index f7742e9..0d3e827 100644
--- a/content/portfolio/what-is-machine-learning.md
+++ b/content/portfolio/what-is-machine-learning.md
@@ -27,15 +27,13 @@ In this article, we will cover:
 To start, it is important to understand the three main categories of machine learning:
 
 1. **Supervised Learning** This type of learning relies on labeled data, where the model learns to map inputs $X$ to outputs $y$. Common tasks include:
-
-- **Classification**: Assigning data to categories (e.g., spam detection).
-- **Regression**: Predicting continuous values (e.g., house price estimation).
+   - **Classification**: Assigning data to categories (e.g., spam detection).
+   - **Regression**: Predicting continuous values (e.g., house price estimation).
 
 2. **Unsupervised Learning** In this case, no labels are provided, and the goal is to find structures or patterns. Common tasks include:
-
-- **Clustering**: Grouping similar data points (e.g., customer segmentation).
-- **Dimensionality Reduction**: Simplifying data while retaining key information (e.g., PCA).
-- **Anomaly Detection**: Identifying unusual data points (e.g., fraud detection).
+   - **Clustering**: Grouping similar data points (e.g., customer segmentation).
+   - **Dimensionality Reduction**: Simplifying data while retaining key information (e.g., PCA).
+   - **Anomaly Detection**: Identifying unusual data points (e.g., fraud detection).
 
 3. **Reinforcement Learning** This learning type involves an agent interacting with an environment. The agent learns by trial and error to maximize cumulative rewards, as seen in robotics and gaming.
 
@@ -48,19 +46,16 @@ With this overview of ML types, let’s now focus on supervised learning, the mo
 Selecting the right supervised learning model is critical and depends on several factors:
 
 1. **Problem Type**
-
-- Is it a regression or classification problem?
-- **Key Point**: Determine if the target variable is continuous or discrete.
+   - Is it a regression or classification problem?
+   - **Key Point**: Determine if the target variable is continuous or discrete.
 
 2. **Problem Complexity**
-
-- Is the relationship between input features and the target variable linear or nonlinear?
-- **Key Point**: Understand whether the data allows for easy predictions or requires more complex models.
+   - Is the relationship between input features and the target variable linear or nonlinear?
+   - **Key Point**: Understand whether the data allows for easy predictions or requires more complex models.
 
 3. **Algorithmic Approach**
-
-- Should you choose a feature-based or similarity-based model?
-- **Key Point**: The choice of the model (e.g., linear regressions vs k-NN) depends on the dataset’s size and complexity.
+   - Should you choose a feature-based or similarity-based model?
+   - **Key Point**: The choice of the model (e.g., linear regressions vs k-NN) depends on the dataset’s size and complexity.
 
 Once the model type is defined, the next step is to delve into the full workflow of developing an ML model.