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applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

aws-machine-learning-university-accelerated-cv

Machine Learning University: Accelerated Computer Vision Class

aws-machine-learning-university-accelerated-nlp

Machine Learning University: Accelerated Natural Language Processing Class

aws-machine-learning-university-accelerated-tab

Machine Learning University: Accelerated Tabular Data Class

aws-machine-learning-university-dte

Machine Learning University: Decision Trees and Ensemble Methods

challenges

ICDSS Advanced Data Science Team Challenges 2019-2020

core_week_2_ip

Week two independent project for the analysis of financial inclusion in East Africa

crime-analysis-

Crime analysis in Kenya using data from twitter.

cs229-python

Exercises and solutions to Stanford CS229 Machine Learning in Python

datasciencecheatsheet

deep-neuro-fuzzy-systems-w-python

Source Code for 'Deep Neuro-Fuzzy Systems with Python' by Himanshu Singh and Yunis Ahmad Lone

dlbook_exercises

Exercises for the Deep Learning textbook at www.deeplearningbook.org

football-predictions-mchezopesa-ltd

german-traffic-sign-classification-using-tensorflow

In this project, I used Python and TensorFlow to classify traffic signs. Dataset used: German Traffic Sign Dataset. This dataset has more than 50,000 images of 43 classes. I was able to reach a +99% validation accuracy, and a 97.3% testing accuracy.

hello-world1

Hello world for setting up github

industrial-machine-learning

Source Code for 'Industrial Machine Learning' by Andreas François Vermeulen

ip-for-week-3

ip_core_week_9

K-NEAREST NEIGHBOR AND NAIVE BAYERS MODELS Problem Statement Implementation of K-nearest neighbor (kNN) classifier and a Naive Bayes classifier to see which model performs better and under which conditions Understanding the Business Context Working on a titanic dataset and a spambase dataset. From titanic dataset we want to predict the survival rate of the people who where involved in that Titanic accident For the spambase dataset, we want to predict who will can the message be classifer as a spam or a genuine message Defining metrices of success I would developed a model that can attain an accuracy of over 80% and challenge my findings to see how best i could have improved my model for a better prediction. Expreimental Design Titanic Data set and SpamBase dataset be used Perform exploratory on the data and feature enginering Build the model and compare their performance Loading libraries to be used to analysis these datasets

kite-python-blog-post-code

Code snippets from Kite blog posts

ml-youtube-courses

A repository to index and organize the latest machine learning courses found on YouTube.

neural-networks

ICDSS Machine Learning Workshop Series: Neural Networks

notebooks

Jupyter notebooks for the Natural Language Processing with Transformers book

notes

Notes On Using Data Science & Artificial Intelligence To Fight For Something That Matters.

r-eda-week1-r

The goal of this analysis is to identify individuals who are most likely to click on a cryptography course advertisement. This project uses R for analysis

recurrent-neural-networks

Learning about and doing projects with recurrent neural networks

suicide

week-14--ip-

This IP is about the analysis of data using R with focus on dimensionality reduction, feature selection , anomality detection and Association Rules

week-4-ip

This is the independent project for week-4 which is the analysis of electric cars sharing and picking stations in the the city of Paris in April 2018

week-4_core-ip

Week 4 IP for the analysis of Autolib company rental car data.

week13-ip

Kira Plastinina is a Russian brand that is sold through a defunct chain of retail stores in Russia, Ukraine, Kazakhstan, Belarus, China, Philippines, and Armenia