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10-steps-to-become-a-data-scientist

📢 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.

2018-cs109b

Harvard CS109b Public Repository

a-2017

Public Repository for cs109a, 2017 edition

adult

adult dataset

ai-project-fraud-detection

Credit card fraud detection using kafka,scikit,flask,cassandra

air-passengers-arima

ARIMA forecasts on the AirPassengers dataset built in Python and Tableau

amazing-feature-engineering

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

appliedml_python_coursera

Material and note of the course of Applied ML in Python

autogbt

AutoGBT is used for AutoML in a lifelong machine learning setting to classify large volume high cardinality data streams under concept-drift. AutoGBT was developed by a joint team ('autodidact.ai') from Flytxt, Indian Institute of Technology Delhi and CSIR-CEERI as a part of NIPS 2018 AutoML for Lifelong Machine Learning Challenge.

autolearn

AutoLearn, a domain independent regression-based feature learning algorithm.

automated-feature-engineering

Automated feature engineering in Python with feature tools

automl-gs

Provide an input CSV and a target field to predict, generate a model + code to run it.

autosmart

awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

aws-machine-learning-university-accelerated-tab

big-data-lite

Samples to the Oracle Big Data Lite VM

boostaroota

A fast xgboost feature selection algorithm

c9-python-getting-started

Sample code for Channel 9 Python for Beginners course

capstone-arvato

chrome_crx

colabcode

Run VSCode (codeserver) on Google Colab or Kaggle Notebooks

coms4995-s20

COMS W4995 Applied Machine Learning - Spring 20

cookbook

The Data Engineering Cookbook

cortana-intelligence-churn-prediction-solution

This repository contains instructions and the necessary code to deploy a churn-prediction automation solution using Cortana Intelligence Suite.

cortana-intelligence-customer360

This repository contains instructions and code to deploy a customer 360 profile solution on Azure stack using the Cortana Intelligence Suite.

cp-ia-intro-ml-2022

Notebooks pour le module "Fondamentaux du Machine Learning" de la formation Chef de Projet IA (2022)

credit_risk_model

A comprehensive credit risk model and scorecard using data from Lending Club

customer-churn-demo-mrs-spark-hdi

This demo demonstrates how to use Microsoft R Server, Azure HDInsight with R on Linux, Azure Machine Learning, Spark, Scala, and Hive to build an end-to-end, cloud solution for Retail Customer Churn.

customer360

Customer 360 analytics powered by MapR

customer360-1

A customer360 script for Big data tactics