This article covers the top three solutions shared by the winners for WNS online hackathon conducted on 14th-16th September.
This article is a collection of the most useful machine learning and deep learning GitHub repositories and Reddit discussions created in November 2018.
Building a Random Forest from Scratch & Understanding Real-World Data Products (ML for Programmers – Part 3)
This article covers different industry applications where a machine learning model can be implemented and necessary steps to follow in building a model.
This detailed article covers an introduction to the Monte Carlo Method of learning using the popular OpenAI Gym library – with Python implementation!
This article lists down the most awesome machine learning and deep learning GitHub repositories and Reddit discussions from October 2018!
An Intuitive Guide to Interpret a Random Forest Model using fastai library (Machine Learning for Programmers – Part 2)
A summary of fast.ai’s course that interprets the results of a random forest model using various techniques like partial dependence & tree interpreters.
MADRaS is a multi-agent extension of Gym-TORCS and is open source, lightweight, easy to install, and has the OpenAI Gym API.
This article covers stock prediction using ML and DL techniques like Moving Average, knn, ARIMA, prophet and LSTM with python codes.
List of must read books on machine learning and artificial intelligence provides an overview to a data scientist and its uses in modeling
An Introduction to Random Forest using the fastai Library (Machine Learning for Programmers – Part 1)
This article provides a comprehensive summary of fast.ai’s machine learning course. It’s a deep dive into the inner workings of the Random Forest algorithm!