1.
Is Python Good for AI?
Why Python is the Language of Choice for AI and ML Development?

2.
Agenda
1 Why Python is the preferred language for AI and ML Development?
PYPL Popularity of Programming Language Index
Features and Advantages of Python
Range of Available Libraries
Soft Learning Curve
Flexible Programming Style
Platform Agnostic
Multiple Visualization Options
Strong Community Support
Convenient Testing and Debugging
Summing Up
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3.
Why Python is the preferred language for AI and ML Development?
For programming AI and ML software, we need a
language that can offer versatility against the complexity
inherent in the AI projects. Most of the Artificial
Intelligence developers prefer Python, the second choice
being JAVA with close competition by R, Prolog, and
Lisp. Other languages being used in AI development,
although not predominantly, are Scala, Julia, and C++.
Usually, it is the preference of developer, available tech-
stack, and project requirement that dictates the choice
of language for AI and ML development. But, Python
wins hands down. We will now dig deep into why
Python is the preferred language for AI and ML
Development.
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4.
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PYPL Popularity of Programming Language Index
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As per PYPL Popularity of
Programming Language index by
GitHub, Python is ranked as the
number one programming
language in Feb 2020.

5.
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Features and Advantages of Python
 Range of Available Libraries
 Soft Learning Curve
 Flexible Programming Style
 Platform Agnostic
 Multiple Visualization Options
 Strong Community Support
 Convenient Testing and Debugging

6.
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Several Python libraries fit the bill for AI
and ML development as they can access,
handle, and transform complex data in
large volumes. A few examples of such
libraries are TensorFlow, Theano, Keras,
Scikit-learn, NumPy, PyTorch, and
Pandas. The growing popularity of AI-
focused libraries has inspired Python
development companies to venture into
Data Science and Machine Learning.
Range of Available Libraries

7.
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Python is easier to learn due to its clear syntax and
readability. Being a high-level general-purpose
programming language that is also simple and
intuitive, it is easily picked up by data-scientists who
are not professional developers.
Usually, AI projects require substantial collaboration
among developers, database architects, and data
scientists. When the whole team has some level of
proficiency with one common language, building a
prototype becomes easier. Therefore, most of the AI
development companies prefer Python as the
primary language for development.
Soft Learning Curve

8.
It is difficult to find a language as flexible as
Python. Developers can choose either scripting
or Object Oriented Programming, and can
combine Python with other languages
seamlessly. Instead of being restricted to a
particular function, Python can be applied for
linking different data structures together or
back-end programming. Because the majority of
code would be verified in the IDE itself, it is
easier to implement algorithms.
Flexible Programming Style
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9.
Python is a free, open-source, and redistributable
programming language. It can be used on
Windows, Macintosh, Linux, Solaris, and more.
Both these factors ensure that the modules and
even the complete program is largely portable and
doesn’t require much modification to run on a
system different from the one on which it was
developed. Due to this, an Artificial Intelligence
development company saves on time and cost of
platform specific programming, accelerating SDLC.
Platform Agnostic
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10.
AI and ML projects require
processing and visualization of huge
volumes of complex data. Therefore,
it is critical for any language to be a
candidate for AI and ML
development to offer data
visualization.
Multiple Visualization Options
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11.
Being an open-source programming language,
Python has extensive documentation easily available
on the internet. There are communities, discussion
forums, and blogs where developers have entered
valuable information in elaborative threads.
Established support community along with flexible
programming style, soft learning curve, and range
of available libraries ensures that even enterprise-
level Artificial Intelligence Python applications enter
the market faster when compared to other
languages such as Scala or Julia.
Strong Community Support
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12.
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With the widespread use of AI in day-to-day
software, it is important to consider the cost of AI
development services. The newly developed AI
software needs to be tested against massive data
with considerable noise. Moreover, tests have to be
thorough to remove human bias if any, because
with such volume of data an error if left
undetected is bound to be amplified. Readily
available talent and established community support
make it relatively inexpensive and easier to test
Python projects.
Convenient Testing and Debugging

13.
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AI and ML are emerging technologies and yet
to achieve a mature tech-stack like mobile app
development. Unless another programming
language emerges more versatile, flexible,
economic, and with an established peer
community; Python will continue to stay the
primary coding language for the major chunk of
AI and ML development projects.
Summing Up

14.
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15.
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