Python for Data Science & Machine Learning

In this repo you'll learn how to use NumPy, Pandas, Seaborn, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more!

Introduction to Anaconda

Anaconda is a popular open-source distribution of Python and R programming languages, specifically designed for scientific computing and data science. We will be using it for data analysis, machine learning, artificial intelligence, and more.

TLDR; Anaconda simplifies package/env management and deployment, making it an essential tool for data scientists and developers.

Quick Guide: Installing Anaconda and Setting Up Your First Environment

Step 1: Download Anaconda

• Visit https://www.anaconda.com/
• Choose the version for your operating system (Windows, macOS, or Linux).

Step 2: Install Anaconda

• Run the downloaded installer.
• Follow the installation instructions:
  • Next
  • Agree to terms
  • Install for just me
  • Check add Anaconda to my PATH env & Register Anaconda as default (if you want)

Step 3: Verify Installation

After installation, open a terminal (or Anaconda Prompt on Windows) and run:

conda --version

Or search for 'Anaconda' in your search bar. You should see the Navigator and the terminal.

Step 4: Create Your First Environment

• Open a terminal or Anaconda Prompt.
• Create a new environment named datascience with the necessary packages, let's do numpy first:

conda create --name datascience numpy

• Activate your env

conda activate datascience

• Testing your env - A new browser window should open, allowing you to start working on your data science projects. Just make sure you are in the right working directory.

jupyter notebook

• Deactivating your env

conda deactivate

Note:

You can install a different version of Python or multiple packages like so:

conda create --name datascience python=3.8 numpy pandas scikit-learn jupyter

In the Anaconda terminal, (base) indicates that you are currently in the default Anaconda environment also known as root, while (datascience) shows that you have activated a custom environment named datascience. Environments allow you to manage dependencies and packages separately for different projects.