kaistha23 Goto Github PK
Name: Gautam kaistha
Type: User
Bio: Data Scientist - Fraud Analytics, Insurance (Personal & Commercial) , Payments fraud, Model Deployment
Location: Bangalore
Name: Gautam kaistha
Type: User
Bio: Data Scientist - Fraud Analytics, Insurance (Personal & Commercial) , Payments fraud, Model Deployment
Location: Bangalore
📢 Ready to learn! ✌ you will learn 10 skills as data scientist: Machine Learning, Deep Learning, Data Cleaning, EDA, Learn Python, Learn python packages such as Numpy, Pandas, Seaborn, Matplotlib, Plotly, Tensorfolw, Theano...., Linear Algebra, Big Data, Analysis Tools and solve some real problems such as predict house prices.
A Python toolkit for rule-based/unsupervised anomaly detection in time series
Official Repo for Google Cloud AI Platform
Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker
This repository contains the lab files and other resources for the free Microsoft course DAT207x: Analyzing and Visualizing Data with Power BI. To learn how to connect, explore, and visualize data with Power BI, sign up for this course on edX.
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
All the cheat sheets you need as a novice machine learning engineer
CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system
An exploration of segment techniques with RFM analysis and K-means clustering.
Cheat Sheets
Plotly's Documentation
UCSanDiegoX edX Course DSE210x Statistics and Probability in Data Science using Python
TensorFlow examples
Data analysis and visualization with PyData ecosystem: Pandas, Matplotlib Numpy, and Seaborn
Repository with files used for the blog post "Predicting Fraud with Autoencoders and Keras"
Model API for GALACTICA
Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Interactive Widgets for the Jupyter Notebook
Deep Learning for humans
A collection of all the datasets that I have analyzed and various algorithms used for training.
Machine Learning Experiments and Work
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
Finding money laundering transactions using frequent association mining algorithm(Apriori) and Graph Traversal techniques.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.