kdmac Goto Github PK
Name: Kuldeep Singh
Type: User
Company: EY
Location: Bengaluru
Name: Kuldeep Singh
Type: User
Company: EY
Location: Bengaluru
Machine Learning Challenge on HackerEarth
This code gives basic understanding how to feed text data into model and how text different word embedding work.
This code will help to understand how we can perform basic statistic and one-hot encoding over categorical variable.
This is one of the clustering technique. This particular code help how cluster algo help to find pattern is data and how these pattern help us to understand data better. Over here i use TITANIC dataset to find find survival pattern in data in different way.
Course Files for Complete Python 3 Bootcamp Course on Udemy
🍟 Stanford CS229: Machine Learning
This repo has all the required materials of the Data science with python video series.
Learning data science
The Leek group guide to data sharing
This code is end to end model building process and tuning of models and finalizing the final model
Plotting Assignment 1 for Exploratory Data Analysis
Getting and cleaning data- final project-week4
Boosting is a method of converting weak learners into strong learners. In boosting, each new tree is a fit on a modified version of the original data set.Gradient Boosting trains many models in a gradual, additive and sequential manner. Over here i am using for regression
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
A case is classified by a majority vote of its neighbors, with the case being assigned to the class most common among-st its K nearest neighbors measured by a distance function.
Linear regression - Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model. Voting - You can train your model using diverse algorithms and then ensemble them to predict the final output. Say, you use a Random Forest Classifier, SVM Classifier, Linear Regression etc.; models are pitted against each other and selected upon best performance by voting using the VotingClassifier Class from sklearn.ensemble. Hard voting is where a model is selected from an ensemble to make the final prediction by a simple majority vote for accuracy.
Prediction of loan defaulter based on more than 5L records using Python, Numpy, Pandas and XGBoost
📺 A place to discover the latest machine learning courses on YouTube.
Azure Machine Learning Lab Notebooks
PDFTableExtract
Principal Component Analysis (PCA) for wine data
Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation which converts a set of correlated variables to a set of uncorrelated variables. PCA is a most widely used tool in exploratory data analysis and in machine learning for predictive models. Moreover, PCA is an unsupervised statistical technique used to examine the interrelations among a set of variables. It is also known as a general factor analysis where regression determines a line of best fit.a principal component can be defined as a linear combination of optimally-weighted observed variables. The output of PCA are these principal components, the number of which is less than or equal to the number of original variables. Less, in case when we wish to discard or reduce the dimensions in our dataset. The PCs possess some useful properties which are listed below: The PCs are essentially the linear combinations of the original variables, the weights vector in this combination is actually the eigenvector found which in turn satisfies the principle of least squares. The PCs are orthogonal, as already discussed. The variation present in the PCs decrease as we move from the 1st PC to the last one, hence the importance.
Repository for Programming Assignment 2 for R Programming on Coursera
This code will help to understand step wise understanding of linear regression model through python.
scikit-learn-mooc
This code help to understand step wise SVM algo for regression model.
Visual Studio Code
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.