Machine Learning

Are you new to Machine Learning? You're not alone. In this page you will find a set of useful articles, videos and blog posts from independent experts around the world that will gently introduce you to the basic concepts and techniques of Machine Learning.

General concepts

Learn what you need to know to get started with Machine Learning in a practical, hands on manner without bogging you down with complex math or theory.

FEB 1 2019

Machine Learning for Everyone

by Vasily Zubarev

A simple introduction for those who want to understand Machine Learning, whether you are a programmer or a manager. Only real-world problems, practical solutions, simple language, and no high-level theorems.

OCT 31 2016

How to Learn Machine Learning in 10 Days

by Sebastian Raschka

10 days can provide enough time to learn the basics of Machine Learning, and even allow a new practitioner to apply some of these skills to their own projects.

OCT 20 2016

A Visual Introduction to Machine Learning

by Stephanie Yee, Tony Chu

What is Machine Learning? See how it works with this animated data visualization.

OCT 19 2016

Machine Learning is Fun!

by Adam Geitgey

The world’s easiest introduction to Machine Learning.

OCT 18 2016

A Few Useful Things to Know about Machine Learning

by Pedro Domingos

Developing successful Machine Learning applications requires some "black art" that is hard to find in textbooks. This article summarizes 12 key ML lessons.

OCT 17 2016

What questions can data science answer?

by Brandon Rohrer

There are only five questions Machine Learning can answer: Is this A or B? Is this weird? How much/how many? How is it organized? What should I do next?

OCT 13 2016

Learning Machine Learning: A beginner's journey

by Murat

A compilation of useful resources to learn about machine learning and deep learning (ML/DL)from scratch.

OCT 10 2016

Machine Learning Algorithms: A Concise Technical Overview

by Matthew Mayo

Short and to-the-point tutorials that cover each single, specific machine learning concept

Supervised learning

Links to give you a glimpse of how to solve classification and regression problems starting with labeled data.

OCT 20 2017

How to Spot a Machine Learning Opportunity, Even If You Aren’t a Data Scientist

by Kathryn Hume

This article presents a brief and simple introduction to Machine Learning and supervised learning.

OCT 19 2016

Learning from Imbalanced Classes

by Tom Fawcet

This post gives insight and concrete advice on how to tackle imbalanced data.

OCT 18 2016

Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?

by Manuel Fernández-Delgado, Eva Cernadas, Senén Barro

The authors evaluate 179 classifiers for different problems to select the top performing algorithms.

OCT 17 2016

Classification and Regression Trees

by Wei-Yin Loh

Overview of how decision tree models learn the patterns to predict categorical values (classification) and continuous numeric values (regression).

OCT 16 2016

Ensemble Methods In Machine Learning

by Thomas G. Dietterich

This paper reviews ensemble methods and explains why ensembles can often perform better than any single classifier.

OCT 16 2016


by Udacity

Short video that explains in a visual way how Bagging works for ensembles.

OCT 16 2016


by Udacity

Short video that explains in a visual way how Boosting works for ensembles.

OCT 16 2016

The Unreasonable Effectiveness of Random Forests

by Ahmed El Deeb

Why the Random Decision Forest is usually the most effective algorithm to solve most cases?

OCT 15 2016

Statistics 101: Logistic Regression

by Brandon Foltz

Series of 6 videos introducing Logistic Regression: from the basics (what it is, when to use it, why we need it), the probabilities, the odds, the odds ratio and the logit formula.

OCT 15 2016

Logistic Regression versus Decision Trees

by cheesinglee

Blog post that explores the differences between Decision Trees and Logistic Regression.

OCT 14 2016

Put Some Confidence in Your Predictions

by josverwoerd

Blog post explaining how to interpret the Confidence and Expected Error in decision tree predictions.

OCT 12 2016

The Basics of Classifier Evaluation, Part 1

by Tom Fawcet

If it’s easy, it’s probably wrong. An introduction of classification models evaluation.

OCT 12 2016

An Introduction to ROC Analysis

by Tom Fawcet

An introduction of ROC graphs, commonly used for comparing classifiers and visualizing their performance.

OCT 11 2016

K-Fold Cross-Validation

by Udacity

Short video to introduce K-Fold Cross-Validation for models.

OCT 11 2016

Predicting with My Model: Is It Safe?

by josverwoerd

How wrong is your model? Or better yet, how right is it? This blog post explains how to evaluate your model using BigML.

NOV 16 2015

How Machines Learn (And You Win)

by Randal S. Olson and R2D3

This article explains what Machine Learning is based on an example of a how a cable company learns which customers might cancel service.

MAR 15 1994

The Basic Ideas in Neural Networks

by David E. Rumelhart , Bernard Widrow , Michael A. Lehr

This paper analyzes the learning procedure to train networks on which all the applications are based.

Unsupervised learning

Teach yourself how you can discover the hidden patterns in your data without the need for labeled data.

OCT 19 2016

Clustering: K-means algorithm

by Victor Lavrenko

Visual explanation of how the k-means cluster algorithm works.

OCT 17 2016

Divining the ‘K’ in K-means Clustering

by ashenfad

Blog post to learn how the G-means cluster algorithm finds the optimal different groups in a dataset.

OCT 11 2016

Isolation Forest

by Fei Tony Liu, Kai Ming Ting

Paper about the state-of-the-art algorithm to detect anomalies: Isolation Forests.

OCT 10 2016

Exploring 250,000+ Movies with Association Discovery

by atakancetinsoy

Blog post explaining a use case to find Associations using movies metadata.

OCT 9 2016

Topic Models

by David Blei

Video lecture to learn the basic concepts of Topic Modelling in general and Latent Dirichlet Allocation in particular.

OCT 9 2016

Association Analysis: Basic Concepts and Algorithms

by Pang-Ning Tan, Michael Steinbach, Vipin Kumar

Article explaining the basics of Association Discovery applied to market basket analysis.

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