Machine Learning – A Wealth of Information

What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly. Source: Expert Systems

In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. Source: Coursera

Machine Learning Articles and Links

Intuitive Machine Learning: Gradient Descent Simplified by An introduction to the ML concept of Gradient Descent.  Straight-forward, easy to understand.  A good place to start or clarify your understanding of the subject.

An Introduction to Gradient Descent and Linear Regression by Matt Nedrich
Basic introduction to Gradient descent.

Machine Learning – Gradient Descent by Mahsa Hassankashi
Includes MATLAB code for evaluating Gradient Descent.

Machine Learning: The Basics by Andrew Gibiansky
A very brief but concise explanation of Gradient Descent.

Machine Learning: Neural Networks by Andrew Gibiansky
A very brief but concise explanation of Neural Networks.

Machine Learning FAQ by Sebastian Reschka
Introductory article with several informative graphics.  Here’s a page of FAQs on Machine Learning.

A Brief Introduction to Gradient Descent by Alykhan Tejani
Some interesting graphic and a concise explanation of Gradient Descent.  Some notation is not conventional, (y,x) rather than (x,y).


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