Why Python For Artificial Intelligence?

Why Python for Artificial Intelligence ?

Role of python In Artificial Intelligence

New to Artificial intelligence. Are you confused to choose which language to use for your AI journey? The list of languages for Artificial Intelligence includes LISP, Prolog, Python, C#, R, Java and few more.

Choosing one language to work on AI is the frustrating and tedious task, choice of language depends upon a lot of factor like personal preference, Previous coding experience, ease of code, availability of developers, Sources for learning, Learning cost.

In this post, Considering the parameters like

  1. Ease of Learning
  2. Python Libraries
  3. Open Source Learning Content
  4. Demand of Python Developers and Average Salary

Explain why python is by far the best choice for AI Developers. i.e Why Python for Artificial Intelligence?

(1) Ease of Learning

Yes, you can learn Python in months. Python is a very easy language compared to other languages. Even a beginner can learn it very easily and fast. The syntax of Python is so well written, you feel comfortable like reading some English text. Each code line explains itself very efficiently.

Just have a look at the following codes in C, Java, and Python to print ‘Hello World’

     C       

  1. #include <stdio.h>
  2. main() {
  3. printf(“Hello World”);
  4. }

   Java

  1. class hello {
  2. public static void main(String []args)
  3. { System.out.println(“Hello World”);
  4.   }
  5. }

    Python    

  1. print (“Hello World”)

” I don’t think so I still need to prove more why python so easy to learn. If you are a beginner python is the best language, to begin with.”

(2) Python libraries

Libraries

So far the popularity python gain most of because of libraries/packages available in Python like NumPy, Pandas, SciPy, Matplotlib, Scikit Learn, etc are extremely useful for the field in AI/Machine Learning/DL. Most of the heavy lifting can be done easily using these python libraries.

Numpy:

Short for Numerical Python, NumPy is the fundamental package required for high-performance scientific computing and data analysis in the Python ecosystem. It’s the foundation on which nearly all of the higher-level tools, such as Pandas and sci-kit-learn, are built. TensorFlow uses NumPy arrays as the fundamental building blocks underpinning Tensor objects and graph flow for deep learning tasks.

NumPy operations are implemented in C, making them super fast. For data science and modern machine learning tasks, this is an invaluable advantage.

Pandas:

Pandas is the most popular library in the scientific Python ecosystem for doing general-purpose data analysis. Pandas is built on Top of numpy, thereby preserving fast execution speed and offering many data engineering features, including:

  • Reading/writing many different data formats eg: csv, json, etc.
  • Calculating across rows and down columns
  • Finding and filling missing data
  • Reshaping data into different forms
  • Combing multiple datasets together
  • Visualization through Matplotlib and Seaborn

By far the fact, I think pandas is the most used library in python.

Matplotlib and Seaborn

Data visualization and storytelling with data are essential skills for every data scientist because it’s critical to be able to communicate insights from analyses to any audience effectively. This is an equally critical part of your machine learning pipeline, as you often have to perform an exploratory analysis of a dataset before deciding to apply a particular machine learning algorithm.Matplotlib

Matplotlib is the most widely used 2D Python visualization library. It’s equipped with a dazzling array of commands and interfaces for producing publication-quality graphics from your data.

Seaborn is another great visualization library focused on statistical plotting. Have more functionality compare to Matplotlib.  Can draw complicated matplotlib graphs easily in seaborn.

You can start with this great tutorial on Seaborn for beginners.

Scikit-learn

Scikit-learn is the most important general machine learning Python package to master. It features various classification, regression, and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

It provides a range of supervised and unsupervised learning algorithms via a consistent interface. The library has a level of robustness and support required for use in production systems. This means it has a deep focus on concerns such as ease of use, code quality, collaboration, documentation, and performance. Look at this gentle introduction to machine learning vocabulary used in the Scikit-learn universe or this article demonstrating machine learning vs artificial intelligence vs deep learning

Libraries for NLP Task:

Today most of the data exist or producing in the form of Text. To process  that data python comes out with dedicated libraries like :

Nltk: The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.

 Gensim:  Gensim is a Python library for topic modeling, document indexing and similarity retrieval with large corpora. The target audience is the natural language processing (NLP)

Textblob: TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.

(3) Open Source Learning Content

Learning ContentThe Learning Journey of artificial intelligence is not soo smooth. To master AI you need to learn a lot and if the resources or source to learn it will available free of cost. Then it proves as a boon.

For Python, the Open Source Learning Contest is easily available Compare to any other language.

In addition to structured massive open online courses (MOOCs), there are a huge number of incredible, free resources available around the web. Here are a few that have helped me during my learning phase :

Here are a few that have helped me during my learning phase :

(4) Demand of Python Developers and Average Salary

According to the 2018 Developer Survey by StackOverflow, Python is the most wanted technology of this year.

Why Python for Artificial Intelligence ?&

It also ranks as the world’s seventh most popular programming language among professional software developers.

Python for AI

Average Python Developer Salary Compared to Other Programming Languages

According to Gooroo, a platform that provides tech skill and salary analytics, Python is one of the highest-paying programming languages in the USA. At $116,379 per year, Python developers are the best-paid software engineers in the country.

Average Python Developer Salary Compared to Other Programming Languages

Skill Average salaries Monthly jobs advertised
Python US$116,379 6,550
Ruby US$115,005 1,080
Java US$112,592 10,443
Perl US$111,928 1,398
C++ US$108,123 3,567
JavaScript US$103,503 8,764
C# US$101,715 4,101
PHP US$94,690 1,664
ASP.NET US$95,551 1,289
C US$95,166 5,639

Conclusion:

What’s more, it is on the rise which means that there is more demand to hire Python Developers since more companies are choosing the platform for Artificial Intelligence.

Through this blog on Why Python for Artificial Intelligence?, we have laid out a roadmap for you to pursue your AI journey. If you really want it, begin today. All the best.

 

About the author

Vikram singh

Founder of Ai Venture,
An artificial intelligence specialist who teaches developers how to get results with modern AI methods via hands-on tutorials.
GANs are my favorite one.

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