With technological advancement, artificial intelligence (AI) has been on the rise across numerous industries. Several organizations are hopping into this trend and actively investing in this domain. The demand for AI is especially spreading exponentially for analyzing and processing complex data. Among several programming languages, Python is the most universally useful language for learning AI. And for the right reasons!
Python contains extensive libraries to cope with the workload by providing pre-written code and functions to developers, enhancing development productivity and the quality of the code. As complex algorithms execute behind AI, Python allows developers to compose reliable, readable frameworks. This makes Python particularly helpful for composing complex AI algorithms.
Various AI-based Python libraries are used to study AI with Python. In this article, we’ll review the top 10 tools to learn AI with Python. But before that, let’s briefly discuss what makes Python the perfect choice for learning AI.
Table of Contents
Why Learn AI with Python?
When working with AI, developers often need to use complex algorithms to make the AI function properly. Python, as a programming language, provides developers with a robust platform to write these complex algorithms in a way that is both reliable and easy to understand. Python provides an extensive range of libraries and frameworks specifically designed for AI and machine learning tasks. These libraries come with pre-built functions and tools for implementing complex algorithms efficiently.
Moreover, Python has a simple and easy-to-understand syntax. Thus, it can be used by both beginners and experienced developers. Its ease of use and readability allow developers to write clear, concise, and maintainable code, which is crucial for maintaining and collaborating on AI projects.
Another benefit of using Python is that it has a large and active community of developers. This community support helps developers troubleshoot issues effectively, share knowledge, and stay updated on the latest advancements in AI technology.
10 Tools to Learn AI with Python
As discussed above, Python is a developer-friendly AI programming language, and it offers a wide set of libraries taking into account different types of projects.
There are hundreds of easily accessible tools for learning AI using Python. In this section, we’ll provide a thorough analysis of the top 10 tools to learn AI with Python. Our list contains both open-source and commercially available tools.
TensorFlow
TensorFlow takes the first place on our list because of its great features. The machine learning framework contains an extensive suite of libraries and a flexible ecosystem of tools that aid developers in composing and deploying AI applications easily. Another plus point is that it’s free of cost and an open-source platform.
Main Features
- Free of cost
- Robust and high-performance framework
- Makes it easier to compose, train, and deploy AI and machine learning models.
- Supports deep neural networks
- It provides scalable computations across enormous data sets.
- It can be utilized either through script tags or through NPM.
PyTorch
There is always tough competition to secure the top spot, and PyTorch is not far behind. PyTorch contains various open-source libraries that are beneficial for composing and training neural networks based on AI models.
Main Features
- It provides two important features, i.e. n-dimensional Tensor and automatic differentiation. These features help develop and train neural networks efficiently.
- Easy to learn.
- It supports dynamic computations, which permits huge flexibility in building complex applications.
- It provides features and tools for performance optimization. It facilitates faster and more efficient execution of machine learning tasks.
Also Read: What’s The Best Python Data Scraping API?
Keras
Keras Python is basically an open-source API used to build neural networks and AI applications. It behaves like an interface for the TensorFlow library. Keras is recognized for using the best techniques to reduce cognitive load.
Main Features
- It supports a combination of networks. Developers can create and integrate various types of neural networks within a single model.
- It can be used to design and build neural networks.
- It can be executable on the CPU as well as GPU.
- It supports fast prototyping of models.
- Keras provides an extensible interface in order to increase modularity and expressiveness.
Scikit-learn
Next on our list of top tools to learn AI with Python is Scikit-learn, a free machine learning development library for Python. It is a very simple and efficient tool for predictive analysis of data. It can be easily accessible to everyone. Moreover, Scikit-learn can be built on NumPy, SciPy, and Matplotlib.
Main Features
- It is free of cost.
- It helps with data mining and data analysis.
- Easy to learn and use.
- Scikit-learn offers numerous algorithms and models for classification, clustering, dimensional reduction, and preprocessing.
- Supports advanced probability modelling.
NumPy
NumPy, also referred to as Numerical Python, is a standard library for Python that is widely used to work with multi-dimensional matrices and arrays. The library provides an array object which is approximately 50 times faster than traditional Python lists.
Main Features
- NumPy is fast and versatile.
- Supports high-performance N-dimensional array object
- Can be used to handle computations like sound waves, binary functions, broadcasting functions, etc.
- Easily accessible
Also Read: Build your own Resume Parser Using Python and NLP.
Pandas
Python Pandas is an open-source library that is widely used to manipulate and analyze data. It provides powerful data structures, such as DataFrame and Series, which allow users to manipulate and analyze structured data easily. However, it isn’t only limited to data-related tasks. Pandas can also be used to construct more powerful tools for working with data.
Main Features
- Supports reshaping and pivoting of the dataset.
- Offers filtration options in the dataset and numerous indexing options.
- Allows users to handle complex data calculations by using a few commands.
- Python Pandas can be integrated with other Python libraries and tools to create more comprehensive data analysis pipelines.
- Provides extensive support for working with time series data, including date/time indexing, frequency conversion, resampling, etc.
Seaborn
Seaborn is a library for Python that is used to enhance the visualization of data. It is one of the best tools for creating effective visualizations. Seaborn is built on top of Matplotlib, leveraging its functionality while providing additional features and a higher-level interface for creating sophisticated visualizations.
Main Features
- Provides a high-level interface to draw graphics.
- Can be used to make statistical graphs.
- Seaborn provides functionality for automatically estimating and plotting linear regression models along with their associated confidence intervals.
- Supports high-level abstractions.
Theano
Theano is another great tool to learn AI with Python. It is one of the most used libraries for defining, optimizing, and evaluating numerous mathematical expressions efficiently. It is designed to easily tackle numerous computations necessary for neural network algorithms that can be used in AI.
Main Features
- Using Theano on GPU for deep learning tasks can result in faster and more efficient computations compared to a CPU.
- Has the capability to deal with computations done in neural networks
- Supports tight integration with NumPy
- Provides optimizations in terms of speed and stability
- Can be used for extensive unit testing
NLTK
NLTK, also called Natural Language Toolkit, is a Python library used to work with human language data. The library contains powerful algorithms that assist computers in analyzing, preprocessing, and recognizing text written in documents.
Main Features
- Allows users to search keywords in documents as well as classify and tokenize texts
- Free and open-source
- Easy to use
- Provides functionality for recognition, lemmatizing, and stemming words.
MXNet
MXNet, sometimes also called “mix and maximize”, is another open-source framework that can be used to train and deploy neural networks. The framework aims to integrate declarative programming with imperative programming.
Main Features
- Highly scalable, flexible, and lean
- Highly efficient library for deep learning
- Provides quick model training.
- Offers a hybrid front-end.
- Supported by public cloud providers like Amazon, Microsoft Azure, etc.
Final Thoughts
Python is one of the most widely used programming languages for learning AI. This is because it provides developers with a robust platform to write complex AI/ML algorithms in an easy-to-understand way. Python has extensive libraries and frameworks specifically designed for AI and machine learning tasks.
In this article, we’ve provided a thorough analysis of the following tools to learn AI with Python:
- TensorFlow: Enables developers to compose, train, and deploy AI and machine learning models.
- PyTorch: Designed to compose and train neural networks based on AI models.
- Keras: Used to build neural networks and AI applications.
- Scikit-learn: Simple yet efficient library for predictive analysis of data.
- NumPy: Designed to work with multi-dimensional matrices and arrays.
- Pandas: Primarily used to manipulate and analyze data.
- Seaborn: A powerful library for creating effective visualizations.
- Theano: Used for defining, optimizing, and evaluating numerous mathematical expressions efficiently.
- NLTK: Used to work with human language data.
- MXNet: Used to train and deploy neural networks.
Frequently Asked Questions (FAQs)
Can I learn AI with Python?
Python is a great programming language for learning AI. It has a simple syntax, making it accessible to both beginners and experienced developers. Moreover, Python has an extensive ecosystem of libraries and frameworks specifically designed for AI and machine learning tasks.
Can I use Python to make an AI?
Python is the perfect programming language for making AI applications. It provides a wide range of libraries and frameworks that make building, training, and deploying AI and ML models easier.
Is Python enough for AI ML?
While Python is a great choice for creating AI/ML applications, other languages like R and Julia are also used for this purpose.
