Learning

The techniques involved in data science and machine learning require that users have a comprehensive understanding of their fundamentals so that the methods are used properly. Without a rigorous mathematical background to each of the formulas, it is not possible to fully utilize the many algorithms to their full potential. Categories for lessons have been separated into the groups: statistics, data science, and machine learning.

“If a craftsman wants to do good work, he must first sharpen his tools.” – Confucius

• statistics 4
• machine-learning 2
• data-science 1

statistics

Linear Regression

1 minute read

The simplest regression model for beginners.

Maximum Likelihood Estimation

less than 1 minute read

An in-depth look into maximum likelihood estimation (MLE).

Logistic Regression

less than 1 minute read

Understand how to analyze categorical data using logistic regression.

Matrix Notation

6 minute read

A quick introduction to the matrix notation used on this website.

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machine-learning

Feedforward Neural Network

31 minute read

Neural networks for beginners.

The EM Algorithm

less than 1 minute read

This post will cover aspects of the expectation-maximization (EM) algorithm.

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data-science

Data Science

less than 1 minute read

Learn what data science is about.

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