Introduction

Machine Learning is everywhere—from recommendation systems to self-driving cars. But have you ever wondered how these algorithms actually work under the hood? Many tutorials focus on using high-level libraries like scikit-learn and TensorFlow, but understanding the fundamentals requires building ML models from scratch.

That's why I created ML-Algorithms—an open-source repository with clean Python implementations of essential ML algorithms. It’s designed to help beginners and professionals understand, modify, and experiment with machine learning techniques without black-box magic.

🌟 Why This Repo Stands Out

  • 🔍 Clear, well-structured implementations for easy learning.
  • 📝 Jupyter Notebooks for interactive exploration.
  • ⚡ Minimal dependencies—just Python & NumPy.
  • 🎯 Beginner-friendly yet deep enough for pros.

💬 Join the conversation: Share your thoughts, ask questions, and contribute ideas in the GitHub Discussions.

🔎 What’s Inside?

The repository includes well-documented Python implementations of:

✅ Supervised Learning (Linear Regression, Decision Trees)

✅ Unsupervised Learning (K-Means Clustering, PCA)

✅ Reinforcement Learning (Q-Learning, Deep Q-Network)

✅ Deep Learning (Neural Networks, CNNs)

Each algorithm comes with:

  • Simple Python implementations (no unnecessary dependencies).
  • Clear explanations with comments.
  • Jupyter Notebooks to visualize results.

A Jupyter Notebook displaying a linear regression model visualization. The plot shows blue scatter points representing actual data values and a red regression line fitting the trend. The axes are labeled 'Feature' (x-axis) and 'Target' (y-axis), with a title 'Linear Regression: Predicted vs Actual'.

⚙️ How It Works (Example: Decision Trees 🌳)

Let's break down a simple Decision Tree implementation from the repo.

class DecisionTree:
    def __init__(self, max_depth=None):
        self.max_depth = max_depth

    def fit(self, X, y):
        self.tree = self._grow_tree(X, y)

    def _grow_tree(self, X, y, depth=0):
        if depth == self.max_depth or len(set(y)) == 1:
            return np.argmax(np.bincount(y))

        feature, threshold = self._best_split(X, y)
        left_idx, right_idx = X[:, feature] <= threshold, X[:, feature] > threshold
        return (feature, threshold, 
                self._grow_tree(X[left_idx], y[left_idx], depth + 1),
                self._grow_tree(X[right_idx], y[right_idx], depth + 1))

🔹 What’s happening here?

  • The _grow_tree method recursively splits the dataset based on the best feature.
  • Once it reaches the max depth, it assigns the most common class.
  • Simple, effective, and easy to modify!

📸 Want to see it in action? Here’s an example output visualization:

A Jupyter Notebook displaying a decision tree visualization trained on the Iris dataset. The tree is color-coded, with nodes containing information about petal length, petal width, Gini impurity values, and class labels such as 'setosa', 'versicolor', and 'virginica'. The structure splits based on feature values, leading to leaf nodes with final classifications.

💻 Try It Yourself (Google Colab 🚀)

Want to run the code without setting up anything? Click below to open the repo in Google Colab:

Open in Colab

📣 Boosting Open-Source Visibility – Join the Movement!

🚀 If you find this project valuable, here’s how you can help it grow:

✅ Star the repo on GitHub ⭐

✅ Share the repo on X (Twitter), LinkedIn, or Dev.to

✅ Contribute by adding new algorithms or improvements 🛠️

🌍 Join the discussion & connect with ML enthusiasts!

💬 GitHub Discussions – Ask questions, suggest features, and share insights!

🔗 Follow Me for More ML & AI Content

📢 I share AI research, coding projects, and ML insights on my socials:

📌 X (Twitter): @tomlikestocode

🌍 Personal Website: tomboyle.io

🔗 Let’s connect and build amazing AI projects together!

What’s an ML algorithm you’ve struggled with before? Let me know in the comments! 🚀