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Name: Manish Sahu
Type: User
Bio: An avid reader who loves exploring the depths of Machine Learning and Data Science having deep interest in building data driven products.
Location: New Delhi
Name: Manish Sahu
Type: User
Bio: An avid reader who loves exploring the depths of Machine Learning and Data Science having deep interest in building data driven products.
Location: New Delhi
100 Days of ML Coding
Dimension
Codes related to activities on AV including articles, hackathons and discussions.
Task is to perform Exploratory Data Analysis(EDA) with Uni-variate and Bi-variate Analysis to get insights from the DataSet.
The Portuguese Bank had run a telemarketing campaign in the past, making sales calls for a term-deposit product. Whether a prospect had bought the product or not is mentioned in the column named 'response'. The marketing team wants to launch another campaign, and they want to learn from the past one. You, as an analyst, decide to build a supervised model in R/Python and achieve the following goals: Reduce the marketing cost by X% and acquire Y% of the prospects (compared to random calling), where X and Y are to be maximized Present the financial benefit of this project to the marketing team
we will be using the MNIST dataset, which is a set of 70,000 small images of digits handwritten by high school students and employees of the US Census Bureau. Each image is labeled with the digit it represents. This set has been studied so much that it is often called the “Hello World” of Machine Learning: whenever people come up with a new classification algorithm, they are curious to see how it will perform on MNIST.
Using a dataset comprised of songs of two music genres (Hip-Hop and Rock), I have trained a classifier to distinguish between the two genres based only on track information derived from Echonest (now part of Spotify). use the scikit-learn package to predict whether correctly classify a song's genre based on features such as danceability, energy, acousticness, tempo, etc. It will go over implementations of common algorithms such as PCA, logistic regression, decision trees, and so forth.
Performed CRUD Operation using Python Flask Framework.
Cheat Sheets
DataScience projects for learning : Kaggle challenges, Object Recognition, Parsing, etc.
My solutions to DataCamp projects (now only Python)
Resources of Data Hack Summit 2019
For a dating app there are 4 users (two male & two female), they answer the same 10 onboarding compatibility Qs while setting up their user profiles. Create a ML model using python & AI ML framework of your choice to analyse their matching probability.
Introduction to Deep Neural Networks with Keras and Tensorflow
This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.
Codes used for the hack session in DHS 2019
This is an End to end Machine Learning Project using scikit-learn. The DataSource has been imported through web and link of the DataSet is " https://raw.githubusercontent.com/ageron/handson-ml/master/datasets/housing/housing.csv " . Now it's time to get hands dirty with Data and extract insights from it.
Build a Face Recognition system for Face Verification task using dlib Library.
Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
HCL ML Hiring Challenge - 2020
Predict the sales price for each house. For each Id in the test set, you must predict the value of the Sale Price variable.
Codes for Kaggle Competitions
Login Operation using Python Flask Framework.
A list of machine learning GitHub repos sorted by industry.
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
Codes related to various ML Hackathons
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.