anshikachaturvedi
Excited to collaborate, learn, and push the boundaries of AI together with the global tech community on GitHub. Let's create the future!
United Kingdom
anshikachaturvedi's Stars
datawithdanny/postgresql-for-data-analytics
aws-samples/aws-lex-web-ui
Sample Amazon Lex chat bot web interface
microsoft/AI-For-Beginners
12 Weeks, 24 Lessons, AI for All!
plandex-ai/plandex
AI driven development in your terminal. Designed for large, real-world tasks.
Azure-Samples/cosmosdb-chatgpt
Sample application that combines Azure Cosmos DB with Azure OpenAI ChatGPT service
vimallinuxworld13/ML-workshop-ec2-CV-security
stackup-dev/Circle_PW_Android_Wallet
SimplifyJobs/New-Grad-Positions
A collection of full time roles in SWE, Quant, and PM for new grads.
Sai-Chandar/Symbolic_regression
Regression using Genetic Programming
ml-ai-nlp-ir/bios8366_homeworks
Homework submissions for Chris Fonnesbeck's BIO8366 class
awesomedata/awesome-public-datasets
A topic-centric list of HQ open datasets.
PacktPublishing/Hands-On-Genetic-Algorithms-with-Python
Hands-On Genetic Algorithms with Python, Published by Packt
kiecodes/genetic-algorithms
This repository belongs to the youtube videos "What are Genetic Algorithms" (https://youtu.be/uQj5UNhCPuo) and "Genetic Algorithm from Scratch in Python" (https://youtu.be/nhT56blfRpE). If you haven't seen it, please consider watching part one of this series, to get a better understanding of this code.
EC-KitY/EC-KitY
EC-KitY is a scikit-learn-compatible Python tool kit for doing evolutionary computation.
moshesipper/tiny_gp
Tiny Genetic Programming in Python
wri/global-power-plant-database
A comprehensive, global, open source database of power plants
jenetics/jenetics
Jenetics - Genetic Algorithm, Genetic Programming, Grammatical Evolution, Evolutionary Algorithm, and Multi-objective Optimization
trevorstephens/gplearn
Genetic Programming in Python, with a scikit-learn inspired API
ahmedfgad/GeneticAlgorithmPython
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
DEAP/deap
Distributed Evolutionary Algorithms in Python
Farama-Foundation/Arcade-Learning-Environment
The Arcade Learning Environment (ALE) -- a platform for AI research.
flavius-dinu/tfgpt
scikit-multiflow/scikit-multiflow
A machine learning package for streaming data in Python. The other ancestor of River.
marcotcr/lime
Lime: Explaining the predictions of any machine learning classifier
jojo62000/DeepLearningWithPython_SecondEdition
Source code for all exercises for the book - Deep Learning with Python
Devtown-India/HandsOn-Data-Analysis-and-ML
Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day. Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used. In this project, you will use data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. You will compare the system usage between three large cities: Chicago, New York City, and Washington, DC. Day:1 In this project, Students will make use of Python to explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington. You will write code to import the data and answer interesting questions about it by computing descriptive statistics. They will also write a script that takes in raw input to create an interactive experience in the terminal to present these statistics. Technologies that will be covered are Numpy, Pandas, Matplotlib, Seaborn, Jupyter notebook. We will be giving the students a deep dive into the Data Analytical process Day:2 We will be giving the students an insight into one of the major fields of Machine Learning ie. Time Series forcasting we will be taking them through the relevant theory and make them understand of the importance and different techniques that are available to deal with it. After that we will be working hands on the bike share data set implementing different algorithms and understanding them to the core We aim to provide students an insight into what exactly is the job of a data analyst and get them familiarise to how does the entire data analysis process work. The session will be hosted by Shaurya Sinha a data analyst at Jio and Parag Mittal Software engineer at Microsoft.
thalibarrifqi/KMeans-for-Spotify-Song-Clustering
In this project, I will try to cluster songs from Spotify playlists based on their song feature data. I use a playlist that Spotify created and in that playlist doesn't have a specific genre, I used the "Best of the decade for you" playlist. The KMeans clustering will be used as the machine learning algorithm, beacuse it is simple and easy to understand.
Komal01/phishing-URL-detection
Phishing website detection system provides strong security mechanism to detect and prevent phishing domains from reaching user. This project presents a simple and portable approach to detect spoofed webpages and solve security vulnerabilities using Machine Learning. It can be easily operated by anyone since all the major tasks are happening in the backend. The user is required to provide URL as input to the GUI and click on submit button. The output is shown as “YES” for phishing URL and “NO” for not phished URL. PYTHON DEPENDENCIES: • NumPy, Pandas, Scikit-learn: For Data cleaning, Data analysis and Data modelling. • Pickle: For exporting the model to local machine • Tkinter, Pyqt, QtDesigner: For building up the Graphical User Interface (GUI) of the software. To avoid the pain of installing independent packages and libraries of python, install Anaconda from www.anaconda.com. It is a Python data science platform which has all the ML libraries, Data analysis libraries, Jupyter Notebooks, Spyder etc. built in it which makes it easy to use and efficient. Steps to be followed for running the code of the software: • Install anaconda in the system. • gui.py : It contains the code for the GUI and is linked to other modules of the software. • Feature_extractor.py: It contains the code of Data analysis and data modelling. • Rf_model.py: It contains the trained machine learning model. • Only gui.py is to be run to execute the whole software.
CompCogNeuro/sims
Simulations for the Computational Cognitive Neuroscience textbook
vimallinuxworld13/AWS_workshop_2022_data