Python contains extensive libraries to cope with the workload by providing numerous attributes to developers in order to enhance development productivity as well as the quality of the code. As complex algorithms execute behind AI, Python permits developers to compose reliable and readable frameworks.
With the advancement in technology, artificial intelligence has been on the ascent across numerous industries nowadays. Several organizations hop into this fleeting trend and actively begin investing in this domain. However, the demand for AI is spreading exponentially for analyzing and processing complex data. Among several programming languages, python seems to be the most universally useful language to learn AI.
Python contains extensive libraries to cope with the workload by providing numerous attributes to developers in order to enhance development productivity as well as the quality of the code. As complex algorithms execute behind AI, Python permits developers to compose reliable and readable frameworks. This makes Python particularly helpful for composing complex algorithms for AI.
To study AI with python, various AI-based python libraries are used to provide the best quality of development solutions.
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10 Tools to Learn AI with Python
As discussed above, Python is one of the developer-friendly AI programming languages, and it accompanies the help of a wide set of libraries taking into account all projects. There are hundreds of numerous tools to learn AI using python and can be easily accessible in the market. This article will provide a thorough analysis of the top 10 tools to learn AI with python, containing both open source and commercially available tools.
TensorFlow
On top of the list, TensorFlow takes the place because of its great features. The great thing about it is that it’s free of cost and an open-source platform. The TensorFlow contains extensive-suit of libraries and a flexible ecosystem of tools that aid developers to compose and deploy AI applications easily.
Main Features
- It is free of cost.
- Tensor flow is efficient in performance and coordinating best across industries
- Used to train and compose AI models
- Support 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 a tough competition to be at the top place and the PyTorch tool is not far behind. PyTorch was preliminary developed by Facebook and it contains open source libraries which are beneficial to compose and train neural network based on AI models
Main Features
- It provides two important features i.e. n-dimensional Tensor and automatic differentiation to develop and train neural networks.
- Easy to learn.
- It supports dynamic computations, which permits huge flexibility to build complex applications.
- It provides performance optimization.
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 uses the best techniques to reduce cognitive load.
Main Features
- It supports a combination of networks
- It can be used to design and build neural networks.
- It can be executable on CPU as well as GPU.
- It supports the fast prototyping of models.
- Keras provides an extensible interface in order to increase modularity and expressiveness.
Scikit-learn
Scikit-learn is another free machine learning development library for Python. It is a very simple and efficient tool for predictive and analysis of data. It can be easily accessible to everyone. It can be built on NumPy, SciPy, and matplotlib.
Main Features
- It is free of cost.
- It provides helps in data mining and analysis of data.
- It can be understood easily.
- It offers numerous algorithms and models for classification, clustering, dimensional reduction, and preprocessing.
- Advanced probability modeling.
NumPy
NumPy is a standard library for Python used to work with multi-dimensional matrices and arrays. NumPy abbreviated as Numerical Python. It can be used freely. NumPy provides an array object which is approximately 50 times faster than traditional Python lists.
Main Features
- It supports high-performance N-dimensional array object
- It is fast and versatile.
- It can be used to handle computations like sound waves, binary functions, broadcasting functions, etc.
- It can be easily accessible to developers.
Pandas
Python Pandas also an open-source library that can be used to manipulate and analyze data. It cannot be only limited to tackle data-related tasks, it also provides the best tool to construct more powerful tools for data.
Main Features
- It provides reshaping and pivoting of the dataset.
- It offers filtration options in the dataset and numerous indexing options.
- It supports handling complex data calculations by using a few commands.
Seaborn
Seaborn is a library of Python used to enhance the visualization of data. It is one of the best tools to provide an effective visualization of data and based on matplotlib.
Main Features
- It provides a high-level interface to draw graphics.
- It can be used to make statistical graphs.
- It automatically estimates and plots of linear regression graphs.
- It supports high-level abstractions.
Theano
It is one of the most significant libraries used for defining, optimizing, and evaluating numerous mathematical expressions efficiently. It can be more efficient to work on GPU than on CPU. Theano is intended to easily tackle numerous computations necessary for neural network algorithms that can be used in AI.
Main Features
- It has the capability to deal with computations done in neural networks.
- It supports tight integration with NumPy.
- It provides optimizations in speed and stability.
- It can be used for extensive unit testing.
NLTK
NLTK is abbreviated as Natural Language Toolkit. It is a Python library to work with human language data. NLTK contains an algorithm that assists computers to analyze, preprocess, and recognize text written in documents.
Main Features
- It offers to search keywords in documents as well as classifying and tokenizing of texts.
- It is free, open-source, and easy to use.
- It provides recognition, lemmatizing, and stemming words.
MXNet
MXNet can be abbreviated as “mix and maximize”. The aim is to integrate declarative programming with imperative programming. MXNet is another open-source framework that can be used to train and deploy neural networks.
Main Features
- It is a highly scalable, flexible, lean, and most efficient library for deep learning.
- It provides quick model training.
- It offers a hybrid front-end.
- It is supported by public cloud providers like Amazon, Microsoft Azure, etc
Are you using one of these frameworks? Please write your comments below.
