8 changes by Nuxt Studio

content/portfolio/What is Machine Learning.md

@@ -26,31 +26,55 @@ 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 XXX to outputs yyy. Common tasks include:\* **Classification**: Assigning data to categories (e.g., spam detection).\* **Regression**: Predicting continuous values (e.g., house price estimation).![ML Model Types.png](/portfolio/What%20is%20Machine%20Learning/ML%20Model%20Types.png)
+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:
-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).
+
+- **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).
+
 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.
 
+![ML Model Types.png](/portfolio/ML/types.png)
+
 With this overview of ML types, let’s now focus on supervised learning, the most widely used approach, and explore how to choose the right model.
 
 ## Three Considerations for Choosing a Supervised Learning Model
 
 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.
+1. **Problem Type**
-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.
+
-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.
+- 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.
+
+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.
 
 Once the model type is defined, the next step is to delve into the full workflow of developing an ML model.
 
-# The Typical Workflow in Machine Learning
+## The Typical Workflow in Machine Learning
 
 A machine learning project generally follows these steps:
 
-1. **Data Preparation**\* Splitting data into training and testing sets.\* Preprocessing: scaling, handling missing values, etc.
+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.
+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/What%20is%20Machine%20Learning/Modelization%20in%20Prog.png)
+3. **Prediction and Evaluation** Making predictions on unseen data: `model.predict(X)`.\* Comparing predictions ($\hat{y}$​) with actual values ($y$).
 
-**Transition:** Evaluation is a crucial step to verify the performance of a model. For regression problems, the R² score is a key indicator.
+![Modelization in Prog.png](/portfolio/ML/model.png)
+
+Evaluation is a crucial step to verify the performance of a model. For regression problems, the R² score is a key indicator.
 
 ## Evaluating Models: The R² Score
 
@@ -65,7 +89,7 @@ Where:
 
 A $R^2$ close to 1 indicates a good fit.
 
-![R2 Score.png](/portfolio/What%20is%20Machine%20Learning/R2%20Score.png)
+![R2 Score.png](/portfolio/ML/R2.png)
 
 With these concepts in mind, you are better equipped to understand and apply ML models in your projects.
 

content/portfolio/how-my-website-works.md

@@ -13,7 +13,7 @@ My personal website is an overengineered playground where I tinker, explore new
 
 While it's still fresh in my mind, I wanted to document how this version of the site works, the tools I used to build it, and the challenges I overcame to bring it to its current state.
 
-![Website](/portfolio/how-my-website-works/website.png)
+![Website](/portfolio/website-work/website.png)
 
 ## Ideas and Goals
 
@@ -63,7 +63,7 @@ Nuxt UI aims to provide everything you need for the UI when building a Nuxt app,
 
 #### NuxtHub & Cloudflare workers
 
-![NuxtHub](/portfolio/how-my-website-works/nuxt-hub.png)
+![NuxtHub](/portfolio/website-work/nuxt-hub.png)
 
 NuxtHub is an innovative deployment and management platform tailored for Nuxt, leveraging the power of Cloudflare. Deploy your application effortlessly with database, key-value, and blob storage—all configured seamlessly within your Cloudflare account.
 
@@ -81,7 +81,7 @@ One word : `If you know SQL — you know Drizzle.`
 
 #### Nuxt Studio
 
-![Nuxt Studio](/portfolio/how-my-website-works/nuxt-studio.png)
+![Nuxt Studio](/portfolio/website-work/nuxt-studio.png)
 
 Nuxt Studio introduces a fresh editing experience for your Nuxt Content website, providing limitless customization and a user-friendly interface. Edit your website effortlessly with our editor reminiscent of Notion, fostering seamless collaboration between developers and copywriters. It offers a rich text editor, markdown support, and a live preview, enabling you to create and edit content with ease.