What is the Roadmap for Learning AI using Python as a Programming Language?

The IoT Academy
4 min readMar 20


Roadmap for Learning AI using Python as a Programming Language

The ultimate versatility of Python makes it one of the computer languages most sought after by data scientists, software engineers, and developers. The programming language Python is interpreted and all-purpose. Python can be used in a wide range of disciplines, including data analysis, artificial intelligence, software development, and much more. Python has become the most sought-after language to learn in 2021 is no surprise given its versatility.

A career in artificial intelligence is highly desirable and an increasing number of professionals are deviating toward the field. But how to learn AI in an easy manner?

The answer is simple Python. It is being used for learning artificial intelligence widely and is becoming the most preferred language. This article shows why you should learn AI using Python and how to do it.

What do you mean by AI?

Artificial intelligence, or AI, is the term used to describe machines or systems that simulate human intelligence in order to perform tasks and can iteratively improve themselves based on the information they gather. Learning artificial intelligence can assist in a variety of ways. Several instances are:-

  • AI is used by chatbots to better understand customer needs and provide more practical answers.
  • Intelligent assistants use AI to extract important facts from enormous free-text datasets in order to enhance scheduling.

Reasons for Increasing Demand for AI

A career in artificial intelligence is one of the most pursued ones in recent times. There are numerous reasons for the increase in the demand for the same. Some of them are:-

More computational power: The development of AI models involves extensive computations and the usage of sophisticated neural networks, so implementing AI calls for a lot of computing power. This is now achievable thanks to the development of GPUs. High-level calculations and intricate algorithms are now finally possible to implement.

We have produced an incalculable amount of data over the previous few years. In order to analyze and process this data, machine learning algorithms and other AI approaches must be used.

More Effective Algorithms: Over the last ten years, we have developed cutting-edge algorithms that use Deep Neural Networks.

Why Choose Python?

You should study Python before beginning your programming career for a number of reasons. First of all, Python has a basic syntax and is easily readable. Both novice and experienced programmers can easily learn the language thanks to this. Second, Python is a multipurpose programming language with a wealth of practical libraries and outstanding analytical capabilities. Python’s flexibility allows programmers to dedicate more time to creating various applications.

For programs that need more lines of code in other languages, Python enables developers to write fewer lines of code. For instance, Java programs run faster than Python programs, but Python programs also require much less time to develop because the Python source code is three to five times shorter than the Java source code. Other factors include

  • Less Code: AI implementation requires a massive amount of algorithms. We can avoid writing algorithmic code because Python supports predefined packages. Python also offers a “check as you code” methodology, which lessens the burden of testing the code, to make things even easier.
  • Python comes with hundreds of pre-built libraries that can be used to create a different machine learning and deep learning methods. You only need to perform a single command to install and load the required packages each time you wish to run an algorithm on a batch of data. NumPy, Keras, Tensorflow, PyTorch, and others are examples of pre-built libraries.

Python Libraries for implementing AI

  • Tensorflow: Created by Google, this library is frequently used to create Machine Learning algorithms and carry out complex calculations using Neural Networks.
  • Scikit-Learn: The Python module Scikit-learn is connected to NumPy and SciPy. This is regarded as one of the best libraries for working with complex data.
  • Specifically used for crunching scientific and mathematical data, NumPy is a Python package.
  • Theano is a functional library that successfully computes mathematical statements requiring multi-dimensional arrays.
  • Keras: The implementation of neural networks is made easier by this library. Additionally, it offers the best capabilities for computing models, analyzing data sets, displaying graphs, and a host of other tasks.
  • NLTK is an open-source Python library created primarily for text mining, text analysis, and natural language processing.

The Conclusion

Python is a great language to start learning artificial intelligence. It offers great libraries and easy-to-understand nature. The roadmap to learning AI using Python, though not a cakewalk, is comparatively more seamless than learning AI using other programming languages.



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