Looking to build a career in Python? Want to improve your resume with multiple personal projects on it?
Then this blog of Python projects with source code is for you. You earlier read about the top 5 data science projects ; now, we bring you 12 projects implementing data science with Python. In an interview, a resume with projects shows interest and sincerity.
Spending time on personal projects ultimately proves helpful for your career. In this blog of Python projects, we try our best to include different data science and machine learning libraries of Python to give you a better experience. Keeping you updated with latest technology trends, Join DataFlair on Telegram. Fake news can be dangerous. This is a common way to achieve a certain political agenda.
Social media algorithms often viralize these and create a filter bubble. In this, we will train on a news. It affects movement and can be a cause of tremors and stiffness. This is a neurodegenerative disorder with 5 stages to it, and affects dopamine-producing neurons in the brain. We will also use the libraries scikit-learn, numpy, and pandas.
As we all know that colors are made up of three primary colors: Red, Green, and Blue. Their intensities can be measured between 0 to and by combining them we get 6 million different color values.
This idea of this project is to get the name of the color from the color values. To implement this we use a dataset that has color values and labeled colour names, then we calculate the shortest distance between each colour and display the colour name that has the shortest distance. Work on the interesting Python Project on Color Detection now!! Speech Emotion Recognition SER is an attractive application of data science today as we constantly attempt to give the consumer a better experience.
This includes recognizing human emotion and affective states from speech. In most projects, we use Jupyter Lab to run our code. Ready to build your own model? Histology is the study of the microscopic structure of tissues. Check the entire python project of breast cancer classification with source code. Computer Vision is a field of study enabling computers to see and recognize digital images and videos- this is something only humans and animals are generally capable of.
This involves processes like object recognition, video tracking, motion estimation, and image restoration. Be it your google assistant, Alexa, Siri or some intelligent bot on a website.Do you have a GitHub project?
Now you can sync your releases automatically with SourceForge and take advantage of both platforms. This language called Supernova and it's free-open source.
PWCT comes with many samples, tutorials and movies. EVEAI dll allows embedding inference images from keras models into user-written applications. We deliver the ultimate level of IT service health with simplicity by providing the most granular and intelligent IT service modeling possible, at any scale, and sharing these unique insights with other IT operations management ITOM tools to make them more efficient.
Before You install Zenoss Community ATC-pie is an air traffic control simulation program. It features solo sessions, multi-player network and teacher-student connections.
It renders 3d tower views of airport scenes using FlightGear. Ai -chan is a chat bot you can run offline. You can teach her new conversations, share her memory data, change her appearance, her name, the way she calls you, etc.
Parchis Game in OpenGL. It has GPL license. There are Linux and Windows versions. This application transforms your keyboard into a video control surface for wirecast and your mouse into a PTZ joystick. Big Two is popular poker game in Taiwan. It's coded by python and pygame. This app is single player vs 3 computer players only.
Check whether your smile is considered as as one or not. Test project with a 2D platform game developing in Godot 3. Certain games can be played against an AIwith two players locally, or with increasing levels of difficulty! This honestly had hundreds of hours of development so i hope you enjoy! Python 3 required to play. Carcassonne is classic board game.
Parcassonne is Carcassonne clone written in python 3. This app parcassonne base on pycassonne 0. Parcassonne try to enhance AIbut it still very weak. So I suggest to 1 human vs 4 computer players. This application is using Taiwan rule. AI is using basic minimax tree structure and some rules. This app is single player vs computer only.
Minimum resolution is x Koxinga is the game similar with board game Jamaica for human vs 5 computer player only. Jamaica is not random map and map size is also different 2. Jamaica execute two dice action morning and night actionSkip to main content. Add Your Api. Write for us Become member Login. Become member Login. Source Code. Artificial Intelligence Sample Source Code. The following is a list of sample source code snippets that matched your search term.
Source code snippets are chunks of source code that were found out on the Web that you can cut and paste into your own source code. Whereas most of the sample source code we've curated for our directory is for consuming APIs, we occasionally find something interesting on the API provider side of things. If you know of some sample source code that would be of interest to the ProgrammableWeb community, we'd like to know about it.
Be sure to check our guidelines for making contributions to ProgrammableWeb. Add a new Sample Source Code to our directory.
Return values will always be a full UTC date and time. Arguments passed in are valid so long as at least Financial Currently Zirra has defined over timeseries that are continuously For example, one timeseries might be daily number of It includes id, name, website, tickers, and description.
Every given data point Zirra collects will be tied to one or more companies. Parameters include elapsed how long the request took to processtotal count total number of It includes microsoft as the ID, Microsoft as the name, www. An example paginated response shows id, name, website, tickers, and description. Full code will be available soon to interact with the API.
For additional information, visit Developers can use an API Key to authenticate. Also, the application tests batch SVG parsing. Artificial Intelligence This test is based on Appium.This Python code is meant to demonstrate some of the algorithms in Artificial Intelligence: foundations of computational agentssecond edition.
This is not polished code. It is meant to code representations that work together. It will probably never be polished or when it is polished it is probably time to throw it away and start againand should no be relied on. We make no warranty of any kind, expressed or implied, with regard to these programs or the documentation. The code documentaipython. The whole code base can be downloaded from aipython. Note that the document and the code are derived from the same source, and so should be consistent even up to the same line numbers.
We have a beta prototype of AISpace 2which includes AIPpython and Jupyter Lab integration, with graphical interactions with currently the searching, constraints and planning algorithms. This is under continued development. The above zip file and pdf is probably what you want. For the brave, you can get all of the latex sourcesbut it may not be up-to-date during development and it contains of non-working code under development and extraneous stuff.
The following is a list of all of the files, and may be out of date. Please refer to aipython. Artificial Intelligence 2e foundations of computational agents. Home 1st edition Complete Book Resources Slides. We have followed the following principles: The code should be simple and as close the pseudo-code as possible. We have chosen readbility over efficiency: we have tried to keep the asymptotic complexity as good as possible except in some cases where the more efficient code is an exercisebut have not optimized the constant factors.
The code should work, but it does not include all possible functionality. There are parts missing that could be used as exercises. We make extensive use of list comprehensions, sets, and dictionaries. We try to not use libraries where it is not obvious that the library is appropriate. It is the sort of code that a student could write without extensive searching through libraries. It is designed for Python 3. Not Python 2. You may as well use the latest version. We use a simple method for tracing the code, using a method displaywhich is like print but includes a integer display level.
The user can then set a maximum display level for the corresponding object or classand so has control over the amount of detail printed. We try to not litter the code with too many tracing statements.
The use of display is designed to enable future graphical displays of the algorithms. Separate Files The above zip file and pdf is probably what you want. Python for Artificial Intelligence pythonDemo. This is overridden in the AISpace 2 code above to allow for interaction. Agents and Control agents. See also the reinforcement learning agents. Searching for Solutions searchProblem.
This is the interface that algorithms that search use. It also contains some example graphs. It does not do multi-path pruning or cycle checking.GitHub is home to over 40 million developers working together. Join them to grow your own development teams, manage permissions, and collaborate on projects.
A toolkit for developing and comparing reinforcement learning algorithms. Python OpenAI Baselines: high-quality implementations of reinforcement learning algorithms. Python 9. MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. Python 1. An educational resource to help anyone learn deep reinforcement learning. Python 4. C 2k Unity Machine Learning Agents Toolkit. The Prometheus monitoring system and time series database. A reverse proxy that provides authentication with Google, Github or other provider.
Dataset of GPT-2 outputs for research in detection, biases, and more. Tools for accelerating safe exploration research. Automatic object XML generation for Mujoco. Code for the paper "Emergent Complexity via Multi-agent Competition". This organization has no public members. Skip to content. Sign up. Pinned repositories. Type: All Select type. All Sources Forks Archived Mirrors. Select language. Repositories gym A toolkit for developing and comparing reinforcement learning algorithms.
Python 5, 20, 85 22 Updated Apr 14, Version 1. Read about the new features and fixes from March. The extension makes VS Code an excellent Python editor, and works on any operating system with a variety of Python interpreters.
It leverages all of VS Code's power to provide auto complete and IntelliSense, linting, debugging, and unit testing, along with the ability to easily switch between Python environments, including virtual and conda environments. This article provides only an overview of the different capabilities of the Python extension for VS Code.
For a walkthrough of editing, running, and debugging code, use the button below. The tutorial guides you through installing Python and using the extension.
AIPython: Python Code for AIFCA
You must install a Python interpreter yourself separately from the extension. For a quick install, use Python 3. Once you have a version of Python installed, activate it using the Python: Select Interpreter command. If VS Code doesn't automatically locate the interpreter you're looking for, refer to Environments - Manually specify an interpreter. You can configure the Python extension through settings. See the Settings reference. The Insiders program allows you to try out and automatically install new versions of the Python extension prior to release, including new features and fixes.
To experience Python, create a file using the File Explorer named hello. The Python extension then provides shortcuts to run Python code in the currently selected interpreter Python: Select Interpreter in the Command Palette :. You can also use the Terminal: Create New Integrated Terminal command to create a terminal in which VS Code automatically activates the currently selected interpreter. See Environments below.
For a more specific walkthrough on running code, see the tutorial. The Python extension supports code completion and IntelliSense using the currently selected interpreter. IntelliSense is a general term for a number of features, including intelligent code completion in-context method and variable suggestions across all your files and for built-in and third-party modules. You can also hover over identifiers for more information about them.
IntelliCode provides a set of AI-assisted capabilities for IntelliSense in Python, such as inferring the most relevant auto-completions based on the current code context.
Linting analyzes your Python code for potential errors, making it easy to navigate to and correct different problems. The Python extension can apply a number of different linters including Pylint, Pep8, Flake8, mypy, pydocstyle, prospector, and pylama. See Linting. No more print statement debugging! Set breakpoints, inspect data, and use the debug console as you run your program step by step.It is not an easy task to get into Machine Learning and AI. Given the enormous amount of resources that are available today, many aspiring professionals and enthusiasts find it hard to establish a proper path into the field.
The field is evolving at a constant pace and it is crucial that we keep up with this rapid development. In order to cope with the speed of evolution and innovation that is today so overwhelming, a good way to stay updated and knowledgeable on the advances that have taken place in ML is to engage with the community by contributing to the many open-source projects and tools that are used daily by advanced professionals.
For more information to the same, links have been mentioned as well. TensorFlow is an open source software library that makes the use of data flow graphs for the purpose of numerical computation. Nodes represent mathematical operations in the graph, while the graph edges represent the multidimensional data arrays tensors that flow between them. This flexible architecture allows its users to deploy computation without rewriting code to one or more CPUs or GPUs in a desktop, server, or mobile device.
TensorFlow also includes a data visualization toolkit called TensorBoard. Scikit-learn for machine learning is a module in Python that has been built on the top of SciPy.
The project was started in the year as a Google Summer of Code project, by David Cournapeau and since then there have been many who have contributed. It is currently being maintained by a team of volunteers. Keras that is written in Python is a high-level neural networks API, that is capable of running both on top of either TensorFlow or Theano.
Python Code Examples
It was developed keeping its main focus on enabling fast experimentation. PyTorch is a package in Python that provides its users with the below features:.
Theano is a library written in Python that allows its users to define, optimize, as well as evaluate mathematical expressions that efficiently involve arrays that are multi-dimensional in nature. It can in order to perform efficient symbolic differentiation use GPUs. Gensim is a library in Python for document indexing topic modelling and similarity retrieval with large corpora.
Caffe is a deep learning framework made with keeping in mind expression, speed, as well as modularity. Chainer is a deep learning framework framework that is Python-based and aims at flexibility. It based on the define-by-run approach a. Statsmodels is a package in Python that for statistical computations provides its users with a complement to Scipy that includes descriptive statistics, estimation as well as inference for statistical models. Shogun is Machine learning toolbox which provides its users with a wide range of unified as well as efficient Machine Learning ML methods.
The toolbox very seamlessly allows the combination of multiple data representations, algorithm classes, as well as general purpose tools. All Rights Reserved. Home Article. By: Kirti Bakshi Edit. TensorFlow: TensorFlow is an open source software library that makes the use of data flow graphs for the purpose of numerical computation.
GitHub: Tensorflow 2. Scikit-learn: Scikit-learn for machine learning is a module in Python that has been built on the top of SciPy. GitHub: Scikit-Learn 3.
Keras can be used if you require a deep learning library that: Through user friendliness, modularity, and extensibility allows for easy and fast prototyping. Supports both convolutional networks, recurrent networks, as well as combinations of the two. GitHub: Keras 4. GitHub: PyTorch 5. Theano: Theano is a library written in Python that allows its users to define, optimize, as well as evaluate mathematical expressions that efficiently involve arrays that are multi-dimensional in nature.
GitHub: Theano 6. Gensim: Gensim is a library in Python for document indexing topic modelling and similarity retrieval with large corpora.