1. Introduction

In this session, we’ll walk through everything you need to set up your machine learning environment from scratch. This includes installing essential tools like Anaconda, working with Jupyter Notebook, and setting up a virtual environment to keep your project organized and dependency-free.

2.Tools Needed for Machine Learning Projects

Before diving into coding, there are a few important tools we need to install:

a. Anaconda

Anaconda is a distribution of Python and R for scientific computing. It comes preloaded with libraries like NumPy, Pandas, Matplotlib, and more — all of which are widely used in machine learning and data science.

b. Jupyter Notebook

Jupyter Notebook is a web-based interactive development environment. It allows you to write code in blocks (cells), run each block individually, and visualize data using graphs or tables in the same interface.

c. Python Virtual Environment

Creating a virtual environment helps you manage dependencies specific to a project. This avoids conflicts between libraries and ensures your project is portable and reproducible.

3. Installing Anaconda

Follow these steps to install Anaconda:

1. Visit the official website
   Go to: https://www.anaconda.com
2. Download the installer
   Choose the version suitable for your operating system (Windows, macOS, or Linux). Make sure to select the Python 3.x version.
3. Install Anaconda
   • Run the downloaded installer.
   • Follow the instructions (you can keep most options as default).
   • Let the installation complete.

To check if Anaconda was installed successfully:

conda --version

You should see the installed version number if it’s installed properly.

4. Launching Jupyter Notebook

After installing Anaconda:

1. Open Anaconda Navigator or Anaconda Prompt.
2. Run the following command to start Jupyter Notebook:

jupyter notebook

Your browser will open with the Jupyter interface at:

https://localhost:8888/tree

Here you can create new notebooks (.ipynb files), write and execute Python code, visualize data, and much more.

5. Creating a Virtual Environment (Using Anaconda Prompt)

To isolate your project and manage dependencies separately, create a virtual environment like this:

Step 1: Create the environment

conda create --name ml-env python=3.10

This creates a new environment named ml-env with Python version 3.10.

Step 2: Activate the environment

conda activate ml-env

Now, any libraries you install will be confined to this environment.

Step 3: Install libraries

For example:

pip install numpy pandas matplotlib seaborn scikit-learn

You can install any machine learning libraries you need here.

Step 4: Use the environment in Jupyter Notebook

To make this environment available in Jupyter:

pip install ipykernel
python -m ipykernel install --user --name=ml-env

Now when you open Jupyter Notebook, you’ll see ml-env as an option under “Kernel”.