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1. Introduction
An introduction to Applied Machine Learning
  • What is machine learning (ML)?
    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
    Summary by Dave Lee
    Machine Learning is a "simple" 7-step process designed to create a model which can be used to provide correct answers (most of the time) to a given question. Each of the 7 steps is complex and each provides numerous options that must be considered. It is an iterative process using massive amounts of data. The goal of ML is to provide the ability to use data to predict future outcomes.
  • Andrew Ng: Deep Learning, Self-Taught Learning and Unsupervised Feature Learning
Popular ML algorithms