Python is a great tool in the field of Artificial Intelligence. It has several advantages over other existing tools which make it perfect for many tasks and research projects including general AI, machine learning, natural language and text processing, and neural networks. Below we will consider some pros and cons as well as training and practice tools.
AI with Python
Python is among developers favorites programming languages in AI development because of its syntax simplicity and versatility. Python is very encouraging for machine learning for developers as it is less complex as compared to C++ and Java. It also is a very portable language as it is used on platforms including Linux, Windows, Mac OS and UNIX. It is also likable from its features such as Interactive, interpreted, modular, dynamic, portable and high level which make it more unique than Java.
Also, Python is a Multi-paradigm programming supporting object-oriented, procedural and functional styles of programming. Python supports neural networks and development of NLP solutions thanks to its simple function library and more so ideal structure.
Python AI Pros and Cons
|Rich and extensive variety of library and tools.||Developers accustomed to using Python face difficulty in adjusting to completely different syntax when they try using other languages for AI programming.|
|Supports algorithm testing without having to implement them.||Unlike C++ and Java, python works with the help of an interpreter which makes compilation and execution slower in AI development.|
|Python supporting object-oriented design increases a programmer’s productivity.||Not suitable for mobile computing. For AI meant for mobile applications, Python unsuitable due to its weak language for mobile computing.|
|Compared to Java and C++, Python is faster in development.|
|Less code to write. AI involves a lot of algorithms. Python provides ease of testing – one of the best among competitors. Python helps in easy writing and execution of codes. Python can implement the same logic with as much as 1/5th code as compared to other OOPs languages.|
Python for General AI
These are great tools to be used when using Python for General AI.
|Python for General AI|
|AIMA||Python implementation of algorithms from Russell and Norvig’s ‘Artificial Intelligence: A Modern Approach’|
|EasyAI||Simple Python engine for two-players games with AI (Negamax, transposition tables, game solving).|
|pyDatalog||Logic Programming engine in Python|
|SimpleAI||Python implementation of many of the artificial intelligence algorithms described on the book “Artificial Intelligence, a Modern Approach”. It focuses on providing an easy to use, well documented and tested library.|
Python for Machine Learning
These are great tools to be used when using Python for Machine Learning.
|Python for Machine Learning|
|GraphLab Create||An end-to-end Machine Learning platform with a Python front-end and C++ core. It allows you to do data engineering, build ML models, and deploy them. Key design principles: out-of-core computation, fast and robust learning algorithms, easy-to-use Python API, and fast deployment of arbitrary Python objects.|
|Feature Forge||A set of tools for creating and testing machine learning features, with a scikit-learn compatible API.|
|LibSVM||LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification. A Python interface is available by by default.|
|MDP-Toolkit||Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user’s perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. From the scientific developer’s perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.|
|Milk||Milk is a machine learning toolkit in Python. Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. It also performs feature selection. These classifiers can be combined in many ways to form different classification systems.|
|MlPy||mlpy makes extensive use of NumPy to provide fast N-dimensional array manipulation and easy integration of C code. The GNU Scientific Library ( GSL) is also required. It provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification, regression and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping.|
|Monte||Monte (python) is a Python framework for building gradient based learning machines, like neural networks, conditional random fields, logistic regression, etc. Monte contains modules (that hold parameters, a cost-function and a gradient-function) and trainers (that can adapt a module’s parameters by minimizing its cost-function on training data).|
|Orange||Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Extensions for bioinformatics and text mining. Packed with features for data analytics.|
|PyBrain||PyBrain is a modular Machine Learning Library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.|
|PyML||PyML is an interactive object oriented framework for machine learning written in Python. PyML focuses on SVMs and other kernel methods. It is supported on Linux and Mac OS X.|
|scikit-learn||scikit-learn is a Python module integrating classic machine learning algorithms in the tightly-knit world of scientific Python packages (numpy, scipy, matplotlib). It aims to provide simple and efficient solutions to learning problems that are accessible to everybody and reusable in various contexts: machine-learning as a versatile tool for science and engineering.|
|Shogun||The machine learning toolbox’s focus is on large scale kernel methods and especially on Support Vector Machines (SVM) . It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art OCAS, Liblinear, LibSVM, SVMLight, SVMLin and GPDT. Each of the SVMs can be combined with a variety of kernels. The toolbox not only provides efficient implementations of the most common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number of recent string kernels. SHOGUN is implemented in C++ and interfaces to Matlab(tm), R, Octave and Python and is proudly released as Machine Learning Open Source Software|
|SOM||Self-Organizing Maps is a form of machine learning technique which employs unsupervised learning. It means that you don’t need to explicitly tell the SOM about what to learn in the input data. It automatically learns the patterns in input data and organizes the data into different groups.|
|Weka||Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. See here for a tutorial on using Weka from jython.|
|Yalign||Yalign is a friendly tool for extracting parallel sentences from comparable corpora..|
Python for Natural Language & Text Processing
These are great tools to be used when using Python for Natural Language & Text Processing.
|Python for Natural Language & Text Processing|
|gensim||Gensim is a Python framework designed to automatically extract semantic topics from documents, as naturally and painlessly as possible. Gensim aims at processing raw, unstructured digital texts (“plain text”). The unsupervised algorithms in gensim, such as Latent Semantic Analysis, Latent Dirichlet Allocation or Random Projections, discover hidden (latent) semantic structure, based on word co-occurrence patterns within a corpus of training documents. Once these statistical patterns are found, any plain text documents can be succinctly expressed in the new, semantic representation, and queried for topical similarity against other documents and so on.|
|NLTK||Open source Python modules, linguistic data and documentation for research and development in natural language processing and text analytics, with distributions for Windows, Mac OSX and Linux.|
|Quepy||A python framework to transform natural language questions to queries in a database query language.|
Python for Neural Networks
These are great tools to be used when using Python for Neural Networks.
|Python for Neural Networks|
|bpnn.py||Written by Neil Schemenauer, bpnn.py is used by an IBM article entitled “An introduction to neural networks”.|
|FANN||Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. Bindings to more than 15 programming languages are available. An easy to read introduction article and a reference manual accompanies the library with examples and recommendations on how to use the library. Several graphical user interfaces are also available for the library.|
|ffnet||ffnet is a fast and easy-to-use feed-forward neural network training solution for python. Many nice features are implemented: arbitrary network connectivity, automatic data normalization, very efficient (also parallel) training tools, network export to fortran code.|
|neurolab||Neurolab is a simple and powerful Neural Network Library for Python. Contains based neural networks, train algorithms and flexible framework to create and explore other networks. It has the following features: pure python + numpy; API like Neural Network Toolbox (NNT) from MATLAB; interface to use train algorithms from scipy.optimize; flexible network configurations and learning algorithms; and a variety of supported types of Artificial Neural Network and learning algorithms.|
|PyAnn||A Python framework to build artificial neural networks|
|pyrenn||pyrenn is a recurrent neural network toolbox for python (and matlab). It is written in pure python and numpy and allows to create a wide range of (recurrent) neural network configurations for system identification. It is easy to use, well documented and comes with several examples.|
Python AI Jobs
These are some positions which can help you have an idea what recruiters are looking for in terms of experience, coding language, education, certificate … etc.
|Python AI Jobs|
|INDEED||Indeed Python AI Jobs|
|Monster||Monster Python AI Jobs|
Python AI Videos
These are some videos related to Python and artificial intelligence. Enjoy!
Python Code Practice
Would you like to practice your coding skills? Here are some great resources:
|Python Code Practice|
|CodingBat Python||CodingBat Python Practice|
|Learn Python||Interactive Python Coding|
More information: Python offers a huge list of benefits to all. The usage of Python is such that it cannot be limited to only one activity. Its growing popularity has allowed it to enter into some of the most popular and complex processes like Artificial Intelligence (AI), Machine Learning (ML), natural language processing, data science etc. We hope this page was helpful and provided you with some information about AI with Python. Check out our main page for more components of artificial intelligence resources.