How to create AI in python?
Answers
The first step is to get started. Though it sounds a bit stressful and hard, you should understand that building AI in Python will take some time. The amount of time needed depends on your motivation, skills, the level of programming experience, etc.
In order to build AI with Python, you need to have some base understanding of this language. This is not just a popular general purpose programming language. It’s also widely used for machine learning and computing. First of all, install Python. You may do that installing Anaconda, the open source analytics platform. Including the needed packages for machine learning, NumPy, scikit-learn, iPython Notebook, and matplotlib.
If you are searching for some materials on how to boost your Python skills quicker, check out the following books:
Python The Hard Way
Google Developers Python Course
An Introduction to Python for Scientific Computing
Learn X in Y Minutes
If you’ve already got enough experience of programming using Python, you should still check out Python documentation from time to time.
The next step is to boost your machine learning skills. Of course, it’s almost impossible to reach the ultimate understanding of machine learning in a short period of time. Unless you are a genius or a machine like IBM Watson. That’s why it’s better to start with gaining basic machine learning knowledge or improving its level with a help of the following courses: Andrew Ng’s Machine Learning Course, Tom Mitchell Machine Learning Lectures, etc. Everything you need is the basic understanding of machine learning theoretical aspects.
Here is why developers build AI using Python.
When talking about Python, I’ve already mentioned scientific libraries. These Python libraries will be useful when you build AI. For example, you will use NumPy as a container of generic data. Containing an N-dimensional array object, tools for integrating C/C++ code, Fourier transform, random number capabilities, and other functions, NumPy will be one of the most useful packages for your scientific computing.
Another important tool is pandas, an open source library that provides users with easy-to-use data structures and analytic tools for Python. Matplotlib is another service you will like. It’s a 2D plotting library that creates publication quality figures . Among the best matplotlib advantages is the availability of 6 graphical users interface toolkits, web application servers, and Python scripts. Scikit-learn is an efficient tool for data analysis. It’s open source and commercially usable. It’s the most popular general purpose machine learning library.
After you work with scikit-learn, you may take programming AI using Python to the next level and explore k-means clustering.You should also read about decision trees, continuous numeric prediction, logistic regression, etc. If you want to learn more about Python in AI, read about a deep learning framework Caffee and a Python library Theano.
There are Python AI libraries: AIMA, pyDatalog, SimpleAI, EasyAi, etc. There are also Python libraries for machine learning: PyBrain, MDP, scikit, PyML. If you’re searching for natural language and text processing libraries, check out NLTK.
As you see, the importance of Python for AI is obvious. Any machine learning project will benefit from using Python. As AI needs a lot of research, programming artificial intelligence using Python is efficient – you may validate almost every idea with up to thirty code lines.
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Answer:
If you want to create artificial intelligence chatbots in Python, you'll need Artificial Intelligence Markup Language package. Firstly, create a standard startup file with on pattern. Load aiml b. Add random responses that make a dialog interesting.