AI/ML is all hype in 2023, laying down the foundation of technological advancements for the future. While it is not a newly introduced concept (as it was first coined by Arthur Samuel 1959, a former IBM employee), it has seen tremendous growth in the market from November 2021 to the current date. Everyone wants to integrate their mobile apps, web apps, and software with AI/ML to keep up with the trend and benefit from its various features.
What’s the best way to use AI/ML, then?
Python!
Yes, you heard it right. If you have raised any query about the best platform for integrating AI/ML in your existing solutions or creating a new one from scratch, you might have been suggested to go for Python.
Is Python all that good for AI/ML?
Well, the short answer – Yes. The popularity isn’t a hoax, and there are good reasons to consider Python for your AI/ML-based projects. Let’s dive deeper into why Python is good for AI/ML.
Reasons to Choose Python For AI/ML
Python is one of the most robust, scalable high-level, general-purpose programming languages, focusing on code readability and ease of usage. It is used in AI/ML for many compelling reasons. Let’s understand them in greater detail –
Vast Ecosystem and Repository
Python has a rich collection of frameworks and libraries especially dedicated to AI/ML operations. Popular libraries like Pandas, NumPy, and Matplotlib are capable of carrying out seamless data visualization, data manipulation, and analysis tasks, whereas popular Python frameworks such as PyTorch or TensorFlow can be used for developing and training deep neural networks.
Rapid Prototyping
Python focuses on simplicity and expressiveness, which makes rapid prototyping of AI/ML models a breeze. When you hire Python Developers with proven experience in coding with Python, they can quickly implement new ideas, try out relevant hypotheses and keep iterating on the solutions until they find the perfect solution for their project. Since this entire prototyping process can be carried out efficiently, it saves development time and effort.
Code Readability & Simplicity
Python has one of the cleanest, most intuitive, and most logical syntaxes, which makes it a fairly easier programming language to learn and comprehend. This allows developers to focus more on decoding the logic needed to carry out a particular AI/ML code rather than how to write the code for implementing their logic. This gives more room for experimentation, trial-and-error, and quicker solutions.
Versatility in Approach
Python is a versatile programming language that supports various programming paradigms like functional, object-oriented, and procedural programming. This flexibility allows developers to feel more comfortable coding in Python, allowing them to approach their AI/ML projects more confidently and accurately.
Integration Capabilities
Python can be easily integrated with various third-party service providers, frameworks, libraries, and APIs, making it an ideal framework for working on AI/ML projects. It can also be easily integrated into your legacy software as there are multiple ways to do so, like using Python C/C++ extensions, Data Exchange Formats, Command Line Interfaces, and much more.
Large Developer Community and AI/ML Resources
Python has a large developer community and many AI/ML projects that are readily available for inspection, inspiration, and decoding their logic which can help you find relevant support, reusable code snippets, and other such resources via open forums and communities. You can collaborate with other developers and get more brains behind a complex project.
Access to Pretrained Models
Python has many matured and pre-trained AI/ML models through frameworks such as Hugging Face Transformers, TensorFlow Hub, and sci-kit-learn. You can use these pre-trained models to cut down significantly on the time and expense you would incur in training a new model from scratch.
High-Level Language
When we defined Python, we mentioned it’s a high-level programming language. What this means is that Python can be abstracted at low-level details. Hence the developers can focus more on AI/ML details rather than worry about low-level implementation details.
NLP Capabilities
Python has tremendous potential and support for Natural Language Processing tasks. You can use NLP-specific libraries like spaCy, NLTK, and Transformers to leverage their robust NLP-based tools to help carry out activities like semantic analysis, language translation, entity recognition, and more.
Rich Visualization Options
Python has many rich data visualization libraries like Matplotlib, Seaborn, and Plotly that offer a wide range of customizable charts and plots, enabling businesses to understand the complex calculations and outcomes churned out by AI/ML operations more easily and understandably.
Platform Independence
Unlike most programming languages that have a high dependency and vendor lock-in, Python is highly platform independent that provides cross-operating system compatibility, code reusability, chances for collaboration and portability, dedicated virtual environments, integration capabilities with other technologies, CI/CD pipelines, and many other useful features that make it an ideal choice for accommodating to your changing AI/ML requirements.
Open-Based AI/ML Projects based on Python
When answering why Python is for AI/ML, it is important to highlight some of the most popular use cases of Python in existing AI/ML projects. Here are some of the top open-source AI/ML-based projects that Python powers.
1. Fake Review Detector
We read and rely on reviews for products, services, movies, and any product or service we plan to buy. Popular sites for reading such reviews include Yelp, Quora, Trustpilot, and others. However, not all reviews on such sites are genuine; most are marketing gimmicks or paid comments. Using Python, you can create a Fake Review Detector program to safeguard clients against scams and misinformation.
2. Spam Identification
Who isn’t annoyed by spam? With so many custom bots configured to bypass captchas and other forms of validation, spam has become an ongoing issue for most companies and individuals to manually go through these numerous spams, identify them and delete or block them. With Python, you can create a spam identifier tool using Python NLP libraries and specify certain tones or words to identify the mail as potential spam. Based on your existing spam data, you can train your machine-learning model.
3. Customer Perception Identifier
Sentimental analysis is a core part of AI/ML and can be very useful for companies to manage their online reputation. Using Python’s sentimental analysis capabilities, you can create a solution that analyzes users’ comments and reviews about a particular website to understand their perceptions, opinion and likes towards the website.
4. Traffic Analyser
One of AI/ML’s best uses is to detect real-time traffic details for different route options between the user’s start location and final destination. Your Python-based traffic analyzer project should be able to check for factors like the length of a path, traffic density, commute method, and others. It should provide real-time updates to the user to provide them with the most optimal path to reach their destination on time.
Final Words
These are the various reasons to use Python for AI/ML. Hire dedicated developers with expertise in AI/ML andutilize Python to work on your next AI/ML project and gain all the advantages of Python’s robust capabilities and ease of integration.
