Which machine learning algorithm should I use?
This resource is designed primarily for beginner to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest.
Top 10 Machine Learning Algorithms for Beginners - KDnuggets
A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.
5 EBooks to Read Before Getting into A Machine Learning Career - KDnuggets
A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.
Advanced Machine Learning with Basic Excel
In this article, I present a few modern techniques that have been used in various business contexts, comparing performance with traditional methods. The advanc…
7 More Steps to Mastering Machine Learning With Python - KDnuggets
This post is a follow-up to last year's introductory Python machine learning post, which includes a series of tutorials for extending your knowledge beyond the original.
Practical Machine Learning with R and Python – Part 4
This is the 4th installment of my ‘Practical Machine Learning with R and Python’ series. In this part I discuss classification with Support Vector Machines (SVMs), using both a Linear and a Radial basis kernel, and Decision Trees. Further, a closer look is taken at some of the metrics associated with binary classification, namely accuracy … Continue reading Practical Machine Learning with R and Python – Part 4
30 Questions to test a data scientist on K-Nearest Neighbors (kNN)
30 questions you can use to test the knowledge of a data scientist on k-Nearest Neighbours (kNN) algorithm. kNN is commonly used machine learning algorithm.
Stacking models for improved predictions: A case study for housing prices
This blog was originally published on my website. If you have ever competed in a Kaggle competition, you are probably familiar with the use of combining diffe…
Introduction to Bayesian Decision Theory
Whether you are building Machine Learning models or making decisions in everyday life, we always choose the path with the least amount of risk. As humans, we are hardwired to take any action that…
How to Develop Your First XGBoost Model in Python with scikit-learn
XGBoost is an implementation of gradient boosted decision trees designed for speed and performance that is dominative competitive machine learning. In this post you will discover how you can install and create your first XGBoost model in Python. After reading this post you will know: How to install XGBoost on your system for use in Python. […]
A simple deep learning model for stock price prediction using TensorFlow
For a recent hackathon that we did at STATWORX, some of our team members scraped minutely S&P 500 data from the Google Finance API. The data consisted of index as well as stock prices of the S&P’s…
A Gentle Introduction to XGBoost for Applied Machine Learning
XGBoost is an algorithm that has recently been dominating applied machine learning and Kaggle competitions for structured or tabular data. XGBoost is an implementation of gradient boosted decision trees designed for speed and performance. In this post you will discover XGBoost and get a gentle introduction to what is, where it came from and how […]