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. OCT 31 2016

How to Learn Machine Learning in 10 Days

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 25 2016

A Visual Introduction to Machine Learning

What is Machine Learning? See how it works with this animated data visualization. OCT 24 2016

Machine Learning is Fun!

The world’s easiest introduction to Machine Learning. OCT 23 2016

A Few Useful Things to Know about Machine Learning

Developing successful Machine Learning applications requires some "black art" that is hard to find in textbooks. This article summarizes 12 key ML lessons. OCT 22 2016

What questions can data science answer?

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?

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

This article presents a brief and simple introduction to Machine Learning and supervised learning. JUL 13 2017

Machine Learning Crash Course: Part 4 - The Bias-Variance Dilemma

This post explains the Bias-Variance Dilemma that finds the balance between overfitting and underfitting. OCT 24 2016

Learning from Imbalanced Classes

This post gives insight and concrete advice on how to tackle imbalanced data. OCT 23 2016

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

The authors evaluate 179 classifiers for different problems to select the top performing algorithms. OCT 22 2016

The Problem of Overfitting

Most supervised learning methods have a risk of overfitting, i.e., tailoring the model to fit the training data at the expense of generalization. OCT 21 2016

Classification and Regression Trees

Overview of how decision tree models learn the patterns to predict categorical values (classification) and continuous numeric values (regression). OCT 20 2016

Bagging

Short video that explains in a visual way how Bagging works for ensembles. OCT 19 2016

Boosting

Short video that explains in a visual way how Boosting works for ensembles. OCT 17 2016

Ensemble Methods In Machine Learning

This paper reviews ensemble methods and explains why ensembles can often perform better than any single classifier. OCT 16 2016

The Unreasonable Effectiveness of Random Forests

Why the Random Decision Forest is usually the most effective algorithm to solve most cases? OCT 15 2016

Statistics 101: Logistic Regression

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 14 2016

Logistic Regression versus Decision Trees

Blog post that explores the differences between Decision Trees and Logistic Regression. OCT 13 2016

Put Some Confidence in Your Predictions

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

If it’s easy, it’s probably wrong. An introduction of classification models evaluation. OCT 11 2016

An Introduction to ROC Analysis

An introduction of ROC graphs, commonly used for comparing classifiers and visualizing their performance. OCT 10 2016

K-Fold Cross-Validation

Short video to introduce K-Fold Cross-Validation for models. OCT 9 2016

A video from Andrew Ng introducing precision, recall and the F measure to evaluate classifiers performance. NOV 16 2015

How Machines Learn (And You Win)

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

This paper analyzes the learning procedure to train networks on which all the applications are based. MAR 15 1994

The Basic Ideas in Neural Networks

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 24 2016

Clustering: K-means algorithm

Visual explanation of how the k-means cluster algorithm works. OCT 23 2016

Divining the ‘K’ in K-means Clustering

Blog post to learn how the G-means cluster algorithm finds the optimal different groups in a dataset. OCT 22 2016

Anomaly Detection Using Isolation Forests

Video from BigML VP Data Science explaining how the Isolation Forests algorithm can effectively detect anomalies. OCT 21 2016

Isolation Forest

Paper about the state-of-the-art algorithm to detect anomalies: Isolation Forests. OCT 20 2016

Exploring 250,000+ Movies with Association Discovery

Blog post explaining a use case to find Associations using movies metadata. OCT 19 2016

Topic Models

Video lecture to learn the basic concepts of Topic Modelling in general and Latent Dirichlet Allocation in particular. OCT 19 2016

Association Analysis: Basic Concepts and Algorithms

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