Python has emerged as the go-to language for developers who are building apps using Artificial Intelligence (AI), Machine Learning (ML), and Data Analytics (DA). Python’s simplicity, readability, and large number of libraries make it a popular choice for data scientists and machine learning engineers.
In this blog, we will explore why Python is the best choice for building apps using AI, ML, and data analytics.
It’s Open Source
Python is an open source language, which means that the code created with it has been made available for free use. This characteristic makes it accessible to anyone, regardless of experience level or affiliation – even individuals who are unfamiliar with coding can take advantage of its functionality.
Python is a great option for those seeking access to AI-powered solutions and data analytics workflows, as well as for non-techies who wish to embark on a career in this field! An intrinsic element of Python’s open availability is that anyone can utilize and modify any part of its codebase without precluding themselves from making their own alterations – all within the confines of the same project! The Python Enhancement Proposals (PEPs) process is a well-established mechanism for proposing and discussing new features and changes to the language.
The flexibility afforded by Python allows programmers to leverage libraries that cater toward specific tasks such as image processing, natural language processing and statistics – all without having to reinvent the wheel. Moreover, sophisticated features like object-oriented programming (OOD) make ‘Pythonic’ software more reusable across multiple platforms while being simple enough for beginners yet robust enough for experts alike!
Large number of libraries
Python has a vast number of libraries that can be used for AI, ML, and data analytics. These libraries provide easy-to-use APIs for complex operations like matrix multiplication, data manipulation, neural network building, and data visualization.
Some of the most popular libraries include:
- NumPy: NumPy is a library for scientific computing that provides high-performance numerical array manipulation.
- SciPy: SciPy is a library for scientific computing that provides a wide range of mathematical and scientific functions.
- Pandas: Pandas is a library for data analysis that provides high-performance, easy-to-use data structures and data analysis tools.
- Matplotlib: Matplotlib is a library for creating static, animated, and interactive visualizations.
- Seaborn: Seaborn is a library for creating attractive and informative statistical graphics.
- TensorFlow: TensorFlow is an open-source software library for numerical computation using data flow graphs.
- Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.
- PyTorch: PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing.
A quick search on open sourced code repository GitHub, you can find many “Deep Learning Toolbox (DLT)” for Python! This library provides tons of innovative and useful features for machine learning on both desktop and mobile platforms. It offers both ‘one-stop shops’ for accessing pre-built models as well as providing you with the means to construct your own custom solutions based on ML algorithms. You could also leverage it to define the network architecture of your app and deploy it instantaneously; even if you’re not a seasoned developer – just having one accessible option makes things much easier!
Easy to learn and use
Python is an easy-to-learn language with a simple syntax that makes it easy for developers to get started. Its readability and simplicity make it a great choice for beginners who are learning to code. Moreover, the language’s high-level nature makes it easy to express complex operations in fewer lines of code.
Its syntax is straightforward, and its capabilities are vast yet flexible enough for anyone to operate with ease. There’s no need to become an expert in Python; all you require is a basic comprehension of this indispensable piece of software!
Even seasoned programmers can appreciate Python’s simplicity. It comes equipped with an extensive library of ready-made tools and functions that streamline the process of constructing applications swiftly. Additionally, there are ample resources available on the web, both free and paid; moreover, you can find user guides for particular packages for easy reference when needed.
Python has a massive community of developers and data scientists who contribute to the development of libraries and frameworks. The community provides support through online forums, blogs, and open-source code repositories. This support makes it easier for developers to learn and develop their skills.
For example, if you’re a newbie it can be intimidating to contemplate working with C++ or Java. Fortunately Python not only provides ease-of-use for newcomers but also caters to their needs as well! Also, there is no need for any prior knowledge of coding; beginners do not necessarily require extensive understanding of constructs such as variables or loops – this is beneficial even for experienced programmers! As long as one’s primary goal remains the creation of software applications and complies with the laws of software design – Python makes an excellent choice!
The Python subreddit has over 1.1 million members, and there are many other online forums and communities where you can get help with Python.
The Python community has several online courses and tutorials that are available for free or at a nominal cost. These resources provide developers with a structured way to learn Python and its various libraries and frameworks. You can check out some popular online courses and tutorials at ReviewNPrep.
Python is an excellent, cross-platform choice for AI, ML and Data Analytics. You can deploy Python on iOS, Android, Linux and even macOS without sacrificing any functionality whatsoever!
Python’s versatility makes it the ideal solution for developers with a diverse toolbox of technologies. Additionally, it integrates well with other languages like Java and C++ and can be used with popular web frameworks like Django and Flask. This eases the learning curve by allowing you to use Python in a number of projects from one location rather than having to switch between them repeatedly.
It’s also an impressive option for large enterprises who need greater flexibility when it comes to their tech stack. Why limit yourself to one platform or language when you can have access to all?
Python’s flexibility and scalability make it a great choice for developing AI and ML apps. The language can be used for prototyping and can easily be scaled to handle large datasets and complex algorithms. Its flexibility allows developers to experiment with different techniques and models, leading to faster development cycles. This makes it a good choice for projects of all sizes.
Data analytics and visualization
Python boasts an abundance of powerful data analysis tools, which will allow you to delve deeper into your data and extract more insights from it. Python’s libraries like NumPy, Pandas, and Matplotlib make it easy to analyze and visualize data. These libraries provide tools for data manipulation, data cleaning, and data visualization. Python’s simplicity and readability make it easier to develop and communicate insights from data analysis.
Python is undoubtedly one of the most popular programming language. A quick search on job site indeed.com, shows an active 32k Python jobs in the United States, which shows the popularity of this language. It’s an excellent tool for building apps that leverage applications that utilize artificial intelligence, create machine learning models and data analytics technologies.
With its robust set of tools and libraries, Python provides a solid foundation for practitioners seeking to advance their respective industries forward with their creations. Whether you are a beginner or an experienced developer, Python provides the tools and resources you need to build cutting-edge AI and ML apps.
Starting your dev career? Check out this blog to understand Python vs Java Full Stack Development.
We used ChatGPT to answer the top 10 Python libraries that developers should know about.