Practicals on Machine Learning to understand how to use each and every Core Principals and Concepts with real scenarios (examples) for each.

Each '.ipynb' file has a separated example practical which is associated(usage) of a Core concept.

/ ├── requirements.txt ├── .gitignore ├── README.md ├── Resources/ │ └── titanic.csv (for titanic.ipynb | Download from Kaggle) │ └── image.png (for README.md) └── Notebooks/ ├── 1. titanic.ipynb ├── 2. housing_price.ipynb ├── 3. unsupervised_learning.ipynb ├── 4. principal_component_analysis.ipynb └── 5. support_vector_machines.ipynb 

• Machine Learning Core Concept Practicals
  • File Structure
  • Table of Contents
  • Prerequisites
  • Initialization
    • Clone the Repository
    • Create and Activate the Virtual Environment
    • Install the Required Packages
    • Select the Kernel in Visual Studio Code
      • If the kernel is not available, follow these steps:
    • Run the Notebook Cells

• Install Python (version 3.6 or later) and configure a virtual environment.
• Install Visual Studio Code.
• Install the Python extension for Visual Studio Code.
• (Optionally) Install the Jupyter extension for enhanced notebook support.
• Ensure ipykernel is installed for running notebooks in the selected virtual environment.

git clone https://github.com/TYehan/ML-Practiacal.git

• Open the repository in Visual Studio Code.
• Open the terminal in Visual Studio Code (Ctrl + `).

python -m venv .venv

• For Windows, activate with:

.venv\Scripts\activate

• For macOS/Linux, activate with:

source .venv/bin/activate

Install the dependencies before selecting the kernel to ensure that all necessary packages are available:

pip install -r requirements.txt

Since the IPython kernel is installed using the VS Code Jupyter Notebook extension, follow these steps:

• Open any .ipynb file and click on the kernel selection in the top right corner of the notebook

Open any .ipynb file and execute the cells to view the output.