Introduction to AI tools Important Notes - Artificial Intelligence for Class 10

Introduction to Anaconda

We have seen three primary AI domains: data, natural language processing (NLP), and computer vision. Each domain often requires different Python packages and libraries. Installing and managing many packages for different projects can become difficult if done manually using a single global Python installation.

Anaconda

Anaconda is a free and open-source distribution of Python and R designed for scientific computing, data science, machine learning, large-scale data processing, and predictive analytics. It simplifies package management and deployment by providing the conda package manager and by allowing users to create isolated virtual environments with different packages and interpreter versions. This helps avoid conflicts between project dependencies and makes it easier to work across different AI areas that require different libraries.

Anaconda Navigator

Anaconda Navigator is a graphical desktop interface included with Anaconda. It lets users manage conda packages, environments, and channels, and launch applications such as Jupyter Notebook, Spyder, and others without using the command line. Navigator is useful for beginners and for quickly exploring installed tools and environments.

How to Install Anaconda

1. Step 1: Open your web browser and go to https://www.anaconda.com/distribution/.
2. Step 2: Scroll to the downloads section and choose your operating system (for example, Windows).
3. Step 3: Select the installer that matches your system architecture (32-bit or 64-bit) and the recommended Python version. The download will begin.
4. Step 4: Double‑click the downloaded installer to launch it.
5. Step 5: Click Next to proceed through the installer screens.
6. Step 6: Read the license agreement and click I Agree to accept it.
7. Step 7: Choose Just Me unless you are installing for all users (installing for all users requires administrator privileges), then click Next.
8. Step 8: Select the destination folder for the installation and click Next.
9. Step 9: Do not change the PATH options shown by the installer; leave them as recommended and click Next.
10. Step 10: Wait for the installation to complete.
11. Step 11: At the final screen, click Skip or finish as instructed to complete the setup.

Jupyter Notebook

Jupyter Notebook is an interactive, web-based environment used for creating and sharing documents that combine live code, equations, visualisations and narrative text. It is especially useful for developing and demonstrating AI, data-analysis, and machine‑learning projects because it supports a natural, iterative workflow.

Jupyter originated as the successor to the IPython Notebook project. The IPython Notebook prototype was first developed around 2010, and Jupyter expanded the idea into a language-independent system with support for many kernels (programming languages).

What is a Notebook?

A notebook document mixes code cells, output, formatted text (using Markdown), mathematical equations, images, and other rich media in a single file. This format lets you describe the steps of your analysis, run code, and show results immediately beneath the code that produced them. Notebooks are widely used for teaching, exploratory data analysis, reporting, and reproducible research.

Installing Jupyter Notebook (using Anaconda)

The easiest way to install and use Jupyter Notebook is via Anaconda, because the most commonly used libraries and tools are included. Anaconda ships with conda, Jupyter, and other useful packages that speed up setup for data-science and AI work.

To ensure a virtual environment has a Jupyter kernel, open Anaconda Prompt (or a command prompt with conda available) and run the following command to install Jupyter and related tools in the active conda environment:

Command – conda install jupyter nb_conda ipykernel

After installation, open Jupyter Notebook by running:

Command – jupyter notebook

Notes:

• nb_conda is an extension that helps Jupyter recognise conda environments and enables selecting kernels associated with those environments.
• ipykernel provides the IPython kernel for Jupyter so that Python code can be executed inside notebook cells.

Working with Jupyter Notebook

Jupyter Notebooks run on a kernel. A kernel is a process that executes code in a given language and returns results to the notebook interface. The default kernel for Python notebooks is the IPython kernel. When you create or switch to a different conda environment, you should ensure that a kernel is installed in that environment so the notebook can run code using the environment's packages.

To make a newly created conda environment available in Jupyter, activate the environment and install ipykernel if necessary, then register the kernel name. Example commands:

Command – conda activate myenv

Command – conda install ipykernel

Command – python -m ipykernel install --user --name myenv --display-name "Python (myenv)"

After this, choose the kernel named Python (myenv) from the Notebook's kernel menu to run code with that environment's interpreter and libraries.

Features of Jupyter Notebook

Menu Bar

• File: Create a new notebook, open an existing one, rename notebooks, save the notebook, and use Save and Checkpoint to create restore points that you can roll back to.
• Edit: Cut, copy, paste, delete, split and merge cells, and reorder cells.
• View: Toggle the visibility of the header and toolbar, and show or hide line numbers in cells. You can also enable a cell toolbar for additional controls.
• Insert: Insert new cells above or below the currently selected cell.
• Cell: Run a single cell, run selected cells, run all cells, change a cell's type (Code, Markdown, Raw), and clear a cell’s output.
• Kernel: Manage the kernel that runs your code. You can restart the kernel, reconnect, shut it down, or change which kernel the notebook uses.
• Widgets: Save and clear widget state. Widgets are interactive JavaScript components (sliders, buttons, etc.) that you can control from Python code.
• Help: Access keyboard shortcuts, user-interface tours, and reference documentation for Jupyter and its extensions.