6 Basic Advantages of Using Python for AI

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In the recent past, people have worked to create a machine that behaves like a human. The thinking machine is AI’s biggest gift to mankind; the grand entry of this self-propelled machine has suddenly changed the operative rules of the business. 

In recent years, self-driving vehicles, digital assistants, robotic factory staff, and smart cities have proven that intelligent machines are possible. AI has transformed most industry sectors like retail, manufacturing, finance, healthcare, and media and continues to invade new territories.

Advantages of Using Python for AI

Just like in the development of most software applications, a developer has a variety of languages to use in writing AI. The popular ones include Python, C++, Java, Lisp, and Prolog. The most preferred among these is Python because of the following advantages:

1. A Huge Library

Python provides a great choice of libraries which is one of the main reasons for its popularity. A library is a module or a group of modules published by different sources which include a pre-written piece of code that allows users to reach some functionality or perform different actions. Python libraries provide base-level items so developers don’t have to code them from the very beginning every time.

Machine learning requires data processing, and Python’s libraries let you access, handle and transform data. Some of the most common libraries that are used for ML and AI are:

  • Scikit-learn is used for handling basic ML algorithms like clustering, linear and logistic regressions, classification, etc.
  • Pandas are used for high-level data structures and analysis. It allows merging and filtering of data, as well as gathering it from other external sources like Excel, etc.
  • Kera is used for deep learning. It allows fast calculations and prototyping, as it uses the GPU in addition to the CPU of the computer.
  • TensorFlow is used for deep learning and helps in setting up, training, and utilizing artificial neural networks with massive datasets.
  • Matplotlib is used for creating 2D plots, histograms, charts, and many other forms of visualization.
  • NLTK is used for working with computational linguistics, natural language recognition, and processing.
  • Scikit-image is used for image processing.
  • PyBrain is used for neural networks, unsupervised, and reinforcement learning.
  • Caffe is used for deep learning that allows switching between the CPU and the GPU.
  • StatsModels is used for statistical algorithms and data exploration.

2. Easy to Learn Language

Working in the ML and AI industry means dealing with a lot of data that you need to process in the most convenient and effective way. Because of its ease, it allows more data scientists to quickly pick up Python and start using it for AI development without wasting too much effort on learning the language.

Python programming language resembles the everyday English language, and that makes the process of learning easier. It uses simple syntax that allows you to comfortably work with complex systems. In addition to this, there’s a lot of documentation available.

3. The Flexibility of Language

Python is a very flexible language because of the following features:

  • It offers an option to choose either to use OOPs or scripting.
  • There’s no need to recompile the source code. Developers can implement any changes and quickly see the results.
  • Programmers can combine Python and other languages to get the desired results.

Moreover, flexibility allows developers to choose the programming styles with which they are fully comfortable. Python offers the following styles:

  • Imperative style: It consists of commands that describe how a computer should perform these commands. With this style, you define the sequence of computations that happen as a change of the program state.
  • Function style: It is also called declarative because it declares what operations should be performed. It doesn’t consider the program state, compared to the imperative style, it declares statements in the form of mathematical equations.
  • Object-oriented style: It is based on the concept of objects. Although this style is not fully supported by Python, developers can still use this style to a finite degree.
  • Procedural style: It is the most common among beginners, as it proceeds tasks in a step-by-step format. It’s often used for sequencing, iteration, modularization, and selection.

4. Platform Independence

Python is not only comfortable to use and easy to learn but also very versatile. Python for ML development can run on any platform including Windows, macOS, Linus, Unix, and many more. To transfer the process from one platform to another, developers need to implement several small-scale changes and modify some lines of code to create an executable form of code for the chosen platform. 

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Developers can use packages like PyInstaller to prepare their code for running on different platforms.

5. Abundant Community Support

It’s always very helpful when there’s strong community support built around the programming language. Python is an open-source language which means that there are great resources open for programmers starting from beginners and ending with pros.

A lot of Python documentation is available online as well as in Python communities and forums where programmers discuss errors, solve problems, and help each other out.

6. Excellent Visualization Options

As mentioned above, Python has an extensive set of libraries and some of them offer amazing visualization tools. This is of great use in AI as it involves the representation of data in a human-readable format.

Matplotlib is a library for data scientists that allows them to make charts, histograms, and graphs to present data in a more comprehensible and visualized manner.

Following are the most popular libraries that help in amazing visualization of data:

  • Matplotlib: It is an amazing visualization library in Python for 2D plots of arrays. It consists of several plots like line, bar, scatter, histogram, etc.
  • Seaborn: It is an open-source, BSD-licensed Python library providing a high-level API for visualizing the data using the Python programming language. Although Matplotlib is also used for visualization of data it is summarized that if Matplotlib “tries to make easy things easy and hard things possible”, Seaborn “tries to make a well-defined set of hard things easy too”.
  • Ggplot: It is an R plotting system and it lets you layer components to create a complete plot. For instance, you can start with axes, then add points, then a line, a trendline, etc. According to the creator, ggplot isn’t designed for creating highly customized graphics. It sacrifices complexity for a simpler method of plotting. Ggplot is tightly integrated with pandas, so it’s best to store your data in DataFrame when using ggplot.
  • Bokeh: Its strength lies in the ability to create interactive, web-ready plots, which can be easily output as JSON objects, HTML documents, or interactive web applications. Bokeh also supports streaming and real-time data. Bokeh provides three interfaces with varying levels of control to accommodate different user types. The highest level is for creating charts quickly. It includes methods for creating common charts such as bar plots, box plots, and histograms. The middle level has the same specificity as matplotlib and allows you to control the basic building blocks of each chart (the dots in a scatter plot, for example). The lowest level is geared toward developers and software engineers. It has no pre-set defaults and requires you to define every element of the chart. 
  • Pygal: Pygal offers interactive plots that can be embedded in the web browser. Its prime differentiator is the ability to output charts as SVGs. It is best suited for smaller datasets.
  • Plotly: It is an online platform for data visualization. The biggest advantage of plotly is to create graphics like contour plots, dendrograms, and 3D charts.
  • Geoplotlib: It is a tool for creating maps and plotting geographical data. You can use it to create a variety of map types, like choropleths, heatmaps, and dot-density maps. 
  • Gleam: Gleam allows you to turn analyses into interactive web pages using only Python scripts, so you don’t have to know other languages like HTML, CSS, or JavaScript. Gleam works with any Python data visualization library.  Once you’ve created a plot, you can build fields on top of it so users can filter and sort data.
  • Missingno: Dealing with missing data is really a  pain. Missingno allows you to quickly gauge the completeness of a dataset with a visual summary, instead of trudging through a table. You can filter and sort data based on completion or spot correlations with a heatmap or a dendrogram.
  • Leather: It is designed to work with all data types and produces charts as SVGs, so you can scale them without losing image quality. Since this library is relatively new, some of the documentation is still in progress.

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