Setting Up First Machine Learning Environment Using Anaconda Navigator

Anaconda Navigator

Anaconda is an open source and free distribution of R and Python programming language for a machine learning as well as data science projects. Therefore, it is known as a professional data science platform. It contains a powerful environment manager, which provides a different type of Python environment such as a Spyder, Jupyter notebook, and so on.

Benefits

Anaconda is a free and open-source distributer
Support for Windows, Mac OS, and Linux
Manages a lot of Python libraries
Then it provides various environments by virtualization,
Can easily deal with large data computing and,
It works properly without the need for any administrative privileges.

Supported libraries or packages

Numby
Scipy
Scikit learn
Matplotlib
NLTK
Pandas
Bokeh
R essential.

Supported applications

Jupyter notebook
Jupyterlab
Spyder
Orange
QtConsole
R studio
Visual Studio code.

Jupyter notebook

A Jupyter notebook is an open source environment run in the web browser that allows you to create and share documents belonging to the Python and R program. It provides working facilities such as data cleaning, transformation and visualization, static modeling and machine learning. Jupyter is concerned with three types of core languages such as Julia, Python, and R. Its document is a JSON document, which is default connected to the IPython kernel. It supports launching and managing condo packages without using command line commands.

Spyder

Spyder is an acronym for Scientific Python development environment used in machine learning projects developed in Python language. Then it is integrated with open sources software packages such as NumPy, SciPy, Matplotlib, and IPython. It's also called an open source cross-platform IDE. Anaconda Spyder has some essential features like advanced editing, interactive testing, and debugging.

II Program in Jupyter notebook

Step 1 - Launch Jupyter notebook

Open the Anaconda navigator from your system; there is an available number of working environments.
Choose Jupyter notebook and click on “launch”. Then it will appear with your default web browser.

Setting Up First Machine Learning Environment Using Anaconda Navigator

Step 2 - Creating a Python folder

After running the Jupyter notebook in your local machine, click on the “new” icon in the top right corner of the web page for creating a new Python “folder” in your system.

Step 3 - Creating a Python file

Click on new icon create a new "python 3" file (Python program file).

Step 4 - Add codes and Run

After creating a new Python file, enter your codes inside the rectangular box.
Click on “save” and “run” the program (generally Jupyter has autosave). Then the output will be displayed under the coded area.

Program description

The "two" is a variable that will be getting an input, which type of multiplication table you want. If the for loop is determine iteration 1 to 10, then iterate until false itself. Then the “print” statement will be print belongs to your desired multiplication table.

II Program in spyder

Step 1 - Launch Spyder

Open the anaconda navigator.
Choose “spyder” and click on “launch”.

Step 2 - Add codes and Run

Enter your codes in your spyder editor and “save” it.
Click “run” icon then the codes will be running on the python console environment.

Program description

Imports two types of libraries such as numpy library as “np” and matplot library as “plt”. If the x variable contains pi values belonging to the numpy then it's related to the sin and cos variable and their values. Then finally that value is displayed according to the display query “plt.show()” in the console environment.

Conclusion

I hope, you understand how to set an ML environment using Anaconda navigator.

7 Comments

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manikandan Palanisamy7y ago
thanks Elavarasan I have learnt more in my knowledge
Pradeep Selvam7y ago
super article
Kokila Rani7y ago
Simple and clear explanation. great.
Elavarasan R8y ago
thank you all..
Sarathlal Saseendran8y ago
Nice article elavarasan
Abhishek Mishra8y ago
Superb article and useful
Guest User8y ago
Super elavarasan..I understand easily your article.This is useful to me..Thank you to share