Intro_ML Repository - Introduction to Machine Learning 🤖📚

Welcome to the Intro_ML repository! This repository contains seven Jupyter notebook projects completed during the "Introduction to Machine Learning" course for my Master's program. Each notebook explores different machine learning concepts and algorithms, allowing you to follow along with the code and explanations to deepen your understanding.

List of Notebooks 📋

1. K-Nearest Neighbors (KNN) Classifier 🏙️ Explore the KNN algorithm, a simple and effective classification method based on the distance between data points. Understand how to use KNN for classification tasks.

2. Regularization Techniques 🧊 Dive into regularization methods like L1 and L2 regularization, which help prevent overfitting in machine learning models.

3. Central Limit Theorem (CLT) 🔢📏 Explore the Central Limit Theorem, a fundamental concept in probability and statistics. Understand how the sampling distribution of a sample mean approaches a normal distribution as the sample size increases. Observe the practical implications of the CLT through simulations.

4. Transfer Learning and Fine-Tuning 🎓 Learn about Transfer Learning, a technique that leverages pre-trained models to solve new tasks efficiently. Understand how to fine-tune pre-trained models for your specific tasks.

5. Fit and Prediction 📉🔮 This notebook demonstrates the fitting and prediction process in machine learning. Understand how models are trained and used for making predictions.

6. Gaussian Mixture Model (GMM) 🌟 Explore the Gaussian Mixture Model, a probabilistic model used for clustering and density estimation. Learn how to apply GMM to real-world data.

7. Sequential Learning 🔄🧠 This notebook introduces Sequential Learning, a method that involves continuously updating a model with new data. Understand the advantages and challenges of this approach.

How to Use the Notebooks 🚀

To explore the notebooks and gain insights into the concepts and algorithms of machine learning, follow these steps:

1. Clone the repository to your local machine using Git.
2. Ensure you have Jupyter Notebook or Jupyter Lab installed.
3. Open Jupyter Notebook or Jupyter Lab and navigate to the directory where the cloned notebooks are located.
4. Click on a notebook's filename (e.g., KNN_Classifier.ipynb) to open and interact with the notebook.
5. Read the explanatory text, code cells, and comments to understand the concepts and algorithms used in each notebook.
6. Feel free to modify and experiment with the code to deepen your understanding of machine learning.

Let's Connect! 🤝

If you have any questions or need further assistance with the notebooks or machine learning concepts, feel free to reach out to me on LinkedIn. I'm excited to see you explore the world of machine learning!

Happy learning and coding! 🤖📚🚀