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Welcome to my Machine Learning Techniques repository! This is where I showcase my journey in learning and implementing fundamental machine learning algorithms using Python and popular libraries like NumPy, pandas, and Matplotlib. Through this repository, I aim to share not only the code but also the underlying mathematical concepts that form the backbone of these techniques.
As a passionate learner in the field of machine learning, I've been dedicating time to understand and apply various algorithms. This repository is a reflection of my efforts to grasp the core concepts and algorithms in machine learning. From basic concepts to more advanced algorithms like K-means, KNN, Decision Trees, PCA, and more, I've documented my learnings and implementations here.
All the code in this repository has been developed and tested using Google Colab, a cloud-based platform for running Python code, particularly well-suited for machine learning tasks. If you don't have Python and the required libraries installed locally, you can still run and experiment with the code by opening the notebooks directly in Google Colab.
To run the code in Google Colab, simply click on the notebook file (with the extension .ipynb) in the repository's directory. This will open the notebook in a new tab on your web browser through Google Colab. From there, you can execute the code cells and interact with the notebook as if you were using a local Jupyter environment.
If you prefer to run the code locally or have a specific development environment, you can also download the notebooks and run them on your machine. However, I recommend using Google Colab for the best experience, especially if you want to utilize the free GPU resources provided by Google Colab for training machine learning models.
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