Introduction to AI tools Important Notes | Artificial Intelligence for Class 10 PDF Download

Introduction to Anaconda

We’ve looked into three primary AI domains: data, natural language processing, and computer vision. It’s not uncommon for these domains to have distinct packages that need to be installed while creating code.
Even if we can install them all in IDLE, managing them all is difficult.

Anaconda   

Anaconda is a free and open-source distribution of Python used for scientific computing, including data science, machine learning, large-scale data processing, and predictive analytics. It simplifies package management and deployment, allowing users to create virtual environments with different packages and settings. This is particularly useful for working in different areas of AI, such as data analysis, natural language processing, and computer vision, each requiring different packages.

Anaconda Navigator

Anaconda Navigator is a user-friendly desktop interface that comes with Anaconda. It allows users to manage conda packages, environments, and channels, as well as run programs, all without using command line commands. This makes it easier for users to navigate and manage their projects.

How to Install Anaconda

• Step 1: Log on to https://www.anaconda.com/distribution/
• Step 2: Scroll down to the bar with operating system options and click on windows.
• Step 3: Under  Python 3.7 version, select the right option according to the configuration of your pc(32-bit/64-bit). The download will begin.
• Step 4: Double click the installer to launch. 
• Step 5: Click on “Next”. 
• Step 6: Read the license agreement and click on “I Agree”. 
• Step 7: Select an install for “Just Me” unless you’re installing for all users (which requires Windows Administrator privileges) and click “Next”. 
• Step 8: Select destination folder, and click “Next”.
• Step 9: Do not change anything in PATH Options, click “Next”. 
• Step 10: Wait for the installation to complete. 
• Step 11: Click on “Skip” to continue.  

Jupyter Notebook

The Jupyter Notebook is an exceptionally effective tool for developing and presenting AI-related projects in an interactive manner. Jupyter is the successor to the previous IPython Notebook project, which was first released as a prototype in 2010.

What is a Notebook?

A notebook mixes graphics, narrative prose, mathematical equations, and other rich media with code and its output in a single document. Notebooks are becoming an increasingly popular choice at the centre of contemporary data science, analysis, and, increasingly, science at large, thanks to their natural workflow that encourages iterative and rapid improvement.

Installing Jupyter Notebook

Anaconda is the most convenient way to install and use Jupyter Notebook. Anaconda is the most popular  Python data science distribution, and it comes pre-installed with all of the most popular libraries and tools. With Anaconda, we get the Anaconda Navigator, which allows us to scroll through all of the applications that come with it.

Working with Jupyter Notebook

• It is important to have a kernel on which Jupyter Notebook runs in order to use it. In Jupyter, a kernel provides programming language support. Jupyter Notebook’s default kernel is IPython. As a result, whenever we need to use Jupyter Notebook in a virtual environment, we must first install a kernel in the environment where the notebook will operate.
  Open Anaconda Prompt and type the following command to install the kernel:
  Command – conda install jupyter nb_conda ipykernel
• Jupyter is a Jupyter Notebook extension that is installed in this case. nb conda refers to notebook conda, which is an extension to jupyter kernel to set the kernel for a notebook’s execution. Ipykernel is a powerful and interactive  Python shell and a jupyter kernel to work with  python code in Jupyter Notebooks, and nb conda refers to notebook conda, which is an extension to jupyter kernel to set the kernel for a notebook To open the Jupyter Notebook, use the following command after the installation is complete.
   Command – Jupyter notebook 

Feature of Jupyter Notebook

Menu Bar

• File: In the file menu, you can create a new Notebook or open a pre-existing one. This is also where you would go to rename a Notebook. I think the most interesting menu item is the Save and Checkpoint option. This allows you to create checkpoints that you can roll back to if you need to.
• Edit Menu: Here you can cut, copy, and paste cells. This is also where you would go if you wanted to delete, split, or merge a cell. You can reorder cells here too.
• View menu: The View menu is useful for toggling the visibility of the header and toolbar. You can also toggle Line Numbers within cells on or off. This is also where you would go if you want to mess about with the cell’s toolbar.
• Insert menu: The Insert menu is just for inserting cells above or below the currently selected cell.
• Cell menu: The Cell menu allows you to run one cell, a group of cells, or all the cells. You can also go here to change a cell’s type, although the toolbar is more intuitive for that. The other handy feature in this menu is the ability to clear a cell’s output.
• Kernel Menu: The Kernel cell is for working with the kernel that is running in the background. Here you can restart the kernel, reconnect to it, shut it down, or even change which kernel your Notebook is using
• Widgets Menu: The Widgets menu is for saving and clearing widget state. Widgets are basically JavaScript widgets that you can add to your cells to make dynamic content using  Python (or another Kernel)
• Help Menu: Finally, you have the Help menu, which is where you go to learn about the Notebook’s keyboard shortcuts, a user interface tour, and lots of reference material.