Applied Machine Learning

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A compile collection of key concepts, definitions, resources, and tools to help you get started with machine learning.

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1. Introduction

An introduction to Applied Machine Learning

What is machine learning (ML)?

It's a sub-area of artificial intelligence that allows computers to self-learn without having to be explicitly programmed. ML essentially aims to understand patterns in large sets of input data and then predict outputs based on the models it generates.}
How best to learn machine learning

It's easy to get overwhelmed trying to learn machine learning. There's a lot to learn and too many resources that teach the same content in different and sometimes confusing approaches. It's also tough to learn everything about such a vast and rapidly evolving topic. Ideally, once you feel you've got a sound introduction to machine learning, figure out what specific area(s) you want to specialize in and do your own research.
The 7 Steps of Machine Learning

Summary by Udara Jay

How can we tell if a drink is beer or wine? Machine learning, of course!

The goal of machine learning is to create a model that can answer a question correctly, most of the time.

The 7 steps:

1) Gathering Data
2) Data preparation
3) Choosing a model
4) Training
5) Evaluation
6) Parameter Tuning
7) Prediction

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Andrew Ng: Deep Learning, Self-Taught Learning and Unsupervised Feature Learning