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Layman Tutorials in Machine Learning

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Mapping Global Cuisine with Word Embeddings

November 23, 2021Leave a comment

How can we systemically identify analogous food items across cultures? Learn how using word embeddings.

Random Forest Tutorial: Predicting Goals in Soccer

March 29, 2021November 23, 2021Leave a comment

Learn how a random forest model can help us to predict the probability of a goal, with applications ranging from performance appraisal to match-fixing detection.

Layman’s Guide to A/B Testing

July 19, 2017July 29, 20176 Comments

A/B tests help you decide between two options, A and B. Read this step-by-step guide on conducting your own A/B test to make the right decisions.

Artificial Neural Networks Introduction (Part II)

November 3, 2016November 3, 20162 Comments

In the 2nd part of our tutorial on artificial neural networks, we cover 3 techniques to improve prediction accuracy: distortion, mini-batch gradient descent and dropout.

k-Nearest Neighbors & Anomaly Detection Tutorial

September 14, 2016September 22, 20162 Comments

Do you know what gives red and white wine their colors? Use k-NN to discover the chemical make-up that defines typical types of wines, as well as to detect atypical ones.

Random Forest Tutorial: Predicting Crime in San Francisco

August 25, 2016September 21, 202013 Comments

Learn how random forests, an ensemble of decision trees, can help predict where and when a crime will happen in San Francisco, California.

Decision Trees Tutorial

July 27, 2016September 22, 20169 Comments

Decision trees can be used to identify customer profiles or to predict who will resign. Using the Titanic dataset, learn about its advantages and pitfalls, as well as better alternatives.

Principal Component Analysis Tutorial

June 15, 2016October 21, 201923 Comments

You are exploring the nutritional content of food. How can food items be differentiated? How might they be classified? PCA derives underlying variables that help you slice your data for these insights.

Where Will Your Country Stand in World War III?

April 12, 2016September 22, 201613 Comments

Using weapons trade data, we map out who's against who in the complex arena of international politics.

Association Rules and the Apriori Algorithm

April 1, 2016September 22, 201610 Comments

You own a store. How do you discover purchasing patterns, such as which items tend to be bought together? Knowing this can improve your product placement and advertisement.

Artificial Neural Networks (ANN) Introduction

March 13, 2016October 20, 201627 Comments

Modern smartphone apps allow you to recognize handwriting and convert them into typed words. We look at how we can train our own neural network algorithm to do this.

Regression & Correlation Tutorial

January 31, 2016April 26, 201815 Comments

You have employees. But who should you pick to lead them? Learn how to predict leadership potential using multiple sources of personnel data, as well as pitfalls to watch out for.

K-Means Clustering Tutorial

November 30, 2015September 23, 201611 Comments

You have customers. But how should you categorize them to target sales? How many of such categories exist? To answer these questions, we can use cluster analysis.

K-Nearest Neighbor (KNN) Tutorial: Anomaly Detection

June 22, 2015September 23, 2016Leave a comment

Outliers can be detected by algorithms used for predictions. To illustrate, we use the k-nearest neighbor (kNN) clustering algorithm.

Topic Modeling with LDA Introduction

June 21, 2015September 23, 201619 Comments

Latent Dirichlet allocation (LDA) is a technique that automatically discovers topics that a set of documents contain. It is used to analyze large volumes of text efficiently. To find out how it works, check out this tutorial.

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About Algobeans

Algobeans is authored by Annalyn Ng (University of Cambridge) and Kenneth Soo (Stanford University). We noticed that while data science is increasingly used to improve workplace decisions, many people do not have a good understanding of how it works. Our goal is to support anyone who wants to get started in data science. Each tutorial covers the important functions and assumptions of a data science technique, without any math or jargon. We also illustrate these techniques with real-world data and examples.

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