A Linguagem Python se tornou o padrão para o aprendizado de máquina aplicado. Atualmente, existem mais vagas de trabalho para Cientistas de Dados e Engenheiros de Machine Learning que conhecem Python do que para todos as outras linguagens combinadas. E Por Que a Linguagem Python é Tão Popular em Machine Learning e Inteligência Artificial? Embora existam muitas razões para sua onipresença, essas três costumam responder muito bem a pergunta, título deste post. Confira:
Uma das principais razões para a ampla adoção da linguagem Python é sua simplicidade. Embora não seja uma regra rígida, quanto menor a barreira de entrada em uma linguagem de programação, maior será sua adoção. Python é simples, fácil de aprender, de alto nível e de uso geral, podendo ser usada para diversos fins. Isso significa que qualquer um pode aprender. Quanto menos o desenvolvedor precisar se preocupar com o próprio código, mais foco e ênfase poderão ser colocados na busca de soluções.
A segunda e possivelmente a principal razão para a popularidade da linguagem Python são as bibliotecas. Uma biblioteca em Python é um grupo de código pré-agrupado que você pode importar para o seu ambiente para estender a funcionalidade da linguagem. Existem bibliotecas para praticamente todos os aspectos do aprendizado de máquina aplicado. Por exemplo, o Pandas é uma biblioteca para manipulação dados. NumPy uma biblioteca para todos os tipos de operações matemáticas. O Scikit-Learn é uma biblioteca de uso geral para a construção de modelos preditivos e que também possui muitas ferramentas usadas em todo o pipeline de Machine Learning. O Matplotlib é para visualização e Keras/TensorFlow para a construção de modelos de aprendizado profundo (Deep Learning). Também existem muitas bibliotecas para necessidades específicas, como NLTK para processamento de linguagem natural e uma biblioteca chamada BeautifulSoup para web scraping. No dia de lançamento deste post existem mais de 218 mil pacotes Python para quase todas finalidades possíveis e uma grande quantidade especificamente para Machine Learning e IA. Você pode conferir os pacotes no PyPi, o repositório de pacotes Python.
A terceira razão pela qual Python é tão popular é o Jupyter Notebook. Os Jupyter Notebooks são uma maneira poderosa de criar seu código em Python. Um Jupyter Notebook é uma interface web que permite prototipagem rápida e compartilhamento de projetos relacionados a dados. Em vez de escrever e reescrever um programa inteiro, você pode escrever linhas de código e executá-las uma por vez ou em pequenos lotes. Isso torna a codificação mais fácil de depurar e entender, além de permitir a construção de projetos inteiros usando apenas o navegador.
O sucesso do Jupyter Notebook depende de uma forma de programação chamada “Literate Programming” (em uma tradução livre, algo como Programação Alfabetizada). A Literate Programming é um estilo de desenvolvimento de software criado pelo cientista da computação de Stanford, Donald Knuth. Esse tipo de programação enfatiza uma primeira abordagem em prosa, na qual o texto amigável ao homem é colocado com blocos de código. É excelente para demonstração, pesquisa e objetivos de ensino, especialmente para a ciência. Usamos o Jupyter Notebook em quase todos os cursos aqui na DSA onde a linguagem Python é utilizada.
A simplicidade, a legibilidade, as bibliotecas e o ambiente de desenvolvimento integrado fazem da linguagem Python uma das linguagens mais usadas em Machine Learning e IA atualmente.
E por que não começar a aprender Python agora mesmo, em um curso inteiramente gratuito, online e em português, explicado passo a passo desde o básico até a construção de modelos de Machine Learning? O que está esperando? Por que ainda está lendo isso aqui? Clique no link abaixo e comece agora mesmo, pois o curso é gratuito e ainda oferece certificado de conclusão:
Python Fundamentos Para Análise de Dados
Machine learning and artificial intelligence-based projects are obviously what the future holds. We want better personalization, smarter recommendations, and improved search functionality. Our apps can see, hear, and respond – that’s what artificial intelligence (AI) has brought, enhancing the user experience and creating value across many industries.
Now you likely face two questions: How can I bring these experiences to life? and What programming language is used for AI? Consider using Python for AI and machine learning.
What makes Python the best programming language for machine learning and the best programming language for AI?
AI projects differ from traditional software projects. The differences lie in the technology stack, the skills required for an AI-based project, and the necessity of deep research. To implement your AI aspirations, you should use a programming language that is stable, flexible, and has tools available. Python offers all of this, which is why we see lots of Python AI projects today.
From development to deployment and maintenance, Python helps developers be productive and confident about the software they’re building. Benefits that make Python the best fit for machine learning and AI-based projects include simplicity and consistency, access to great libraries and frameworks for AI and machine learning (ML), flexibility, platform independence, and a wide community. These add to the overall popularity of the language.
Simple and consistent
Python offers concise and readable code. While complex algorithms and versatile workflows stand behind machine learning and AI, Python’s simplicity allows developers to write reliable systems. Developers get to put all their effort into solving an ML problem instead of focusing on the technical nuances of the language.
Additionally, Python is appealing to many developers as it’s easy to learn. Python code is understandable by humans, which makes it easier to build models for machine learning.
Many programmers say that Python is more intuitive than other programming languages. Others point out the many frameworks, libraries, and extensions that simplify the implementation of different functionalities. It’s generally accepted that Python is suitable for collaborative implementation when multiple developers are involved. Since Python is a general-purpose language, it can do a set of complex machine learning tasks and enable you to build prototypes quickly that allow you to test your product for machine learning purposes.
Extensive selection of libraries and frameworks
Implementing AI and ML algorithms can be tricky and requires a lot of time. It’s vital to have a well-structured and well-tested environment to enable developers to come up with the best coding solutions.
To reduce development time, programmers turn to a number of Python frameworks and libraries. A software library is pre-written code that developers use to solve common programming tasks. Python, with its rich technology stack, has an extensive set of libraries for artificial intelligence and machine learning. Here are some of them:
- Keras, TensorFlow, and Scikit-learn for machine learning
- NumPy for high-performance scientific computing and data analysis
- SciPy for advanced computing
- Pandas for general-purpose data analysis
- Seaborn for data visualization
Scikit-learn features various classification, regression, and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means, and DBSCAN, and is designed to work with the Python numerical and scientific libraries NumPy and SciPy.
With these solutions, you can develop your product faster. Your development team won’t have to reinvent the wheel and can use an existing library to implement necessary features.
What is Python good for? Here’s a table of сommon AI use cases and technologies that are best suited for them. We recommend using these:
Machine learning | TensorFlow, Keras, Scikit-learn |
Computer vision | OpenCV |
Natural language processing | NLTK, spaCy |
Platform independence
Platform independence refers to a programming language or framework allowing developers to implement things on one machine and use them on another machine without any (or with only minimal) changes. One key to Python’s popularity is that it’s a platform independent language. Python is supported by many platforms including Linux, Windows, and macOS. Python code can be used to create standalone executable programs for most common operating systems, which means that Python software can be easily distributed and used on those operating systems without a Python interpreter.
What’s more, developers usually use services such as Google or Amazon for their computing needs. However, you can often find companies and data scientists who use their own machines with powerful Graphics Processing Units (GPUs) to train their ML models. And the fact that Python is platform independent makes this training a lot cheaper and easier.
Great community and popularity
In the Developer Survey 2020 by Stack Overflow, Python was among the top 5 most popular programming languages, which ultimately means that you can find and hire a development company with the necessary skill set to build your AI-based project.
In the Python Developers Survey 2020, we observe that Python is commonly used for web development. At first glance, web development prevails, accounting for over 26% of the use cases shown in the image below. However, if you combine data science and machine learning, they make up a stunning 27%.
Online repositories contain over 140,000 custom-built Python software packages. Scientific Python packages such as Numpy, Scipy, and Matplotlib can be installed in a program running on Python. These packages cater to machine learning and help developers detect patterns in big sets of data. Python is so reliable that Google uses it for crawling web pages, Pixar uses it for producing movies, and Spotify uses it for recommending songs.
It’s a well-known fact that the Python AI community has grown across the globe. There are Python forums and an active exchange of experience related to machine learning solutions. For any task you may have, the chance is pretty high that someone else out there has dealt with the same problem. You can find advice and guidance from developers. You won’t be alone and are sure to find the best solution to your specific needs if you turn to the Python community.
Other AI programming languages
AI is still developing and growing, and there are several languages that dominate the development landscape. Here we offer a list of programming languages that provide ecosystems for developers to build projects with AI and machine learning.
R
R is generally applied when you need to analyze and manipulate data for statistical purposes. R has packages such as Gmodels, Class, Tm, and RODBC that are commonly used for building machine learning projects. These packages allow developers to implement machine learning algorithms without extra hassle and let them quickly implement business logic.
R was created by statisticians to meet their needs. This language can give you in-depth statistical analysis whether you’re handling data from an IoT device or analyzing financial models.
What’s more, if your task requires high-quality graphs and charts, you may want to use R. With ggplot2, ggvis, googleVis, Shiny, rCharts, and other packages, R’s capabilities are greatly extended, helping you turn visuals into interactive web apps.
Compared to Python, R has a reputation for being slow and lagging when it comes to large-scale data products. It’s better to use Python or Java, with its flexibility, for actual product development.
Scala
Scala is invaluable when it comes to big data. It offers data scientists an array of tools such as Saddle, Scalalab, and Breeze. Scala has great concurrency support, which helps with processing large amounts of data. Since Scala runs on the JVM, it goes beyond all limits hand in hand with Hadoop, an open source distributed processing framework that manages data processing and storage for big data applications running in clustered systems. Despite fewer machine learning tools compared to Python and R, Scala is highly maintainable.
Julia
If you need to build a solution for high-performance computing and analysis, you might want to consider Julia. Julia has a similar syntax to Python and was designed to handle numerical computing tasks. Julia provides support for deep learning via the TensorFlow.jl wrapper and the Mocha framework.
However, the language is not supported by many libraries and doesn’t yet have a strong community like Python because it’s relatively new.
Java
Another language worth mentioning is Java. Java is object-oriented, portable, maintainable, and transparent. It’s supported by numerous libraries such as WEKA and Rapidminer.
Java is widespread when it comes to natural language processing, search algorithms, and neural networks. It allows you to quickly build large-scale systems with excellent performance.
But if you want to perform statistical modeling and visualization, then Java is the last language you want to use. Even though there are some Java packages that support statistical modeling and visualization, they aren’t sufficient. Python, on the other hand, has advanced tools that are well supported by the community.
At Steelkiwi, we think that the Python ecosystem is well-suited for AI-based projects. Python, with its simplicity, large community, and tools allows developers to build architectures that are close to perfection while keeping the focus on business-driven tasks.
Python as the best language for AI development
Spam filters, recommendation systems, search engines, personal assistants, and fraud detection systems are all made possible by AI and machine learning, and there are definitely more things to come. Product owners want to build apps that perform well. This requires coming up with algorithms that process information intelligently, making software act like a human.
We’re Python practitioners and believe it’s a language that is well-suited for AI and machine learning. If you’re still wondering Is Python good for AI? or if you want to combine Python and machine learning in your product, contact us for the advice and assistance you need.