A Machine Learning Guide for Average Humans

Posted by alexis-sanders

Machine learning (ML) has grown consistently in worldwide prevalence. Its implications have stretched from small, seemingly inconsequential victories to groundbreaking discoveries. The SEO community is no exception. An understanding and intuition of machine learning can support our understanding of the challenges and solutions Google's engineers are facing, while also opening our minds to ML's broader implications.

The advantages of gaining an general understanding of machine learning include:

• Gaining empathy for engineers, who are ultimately trying to establish the best results for users
• Understanding what problems machines are solving for, their current capabilities and scientists' goals
• Understanding the competitive ecosystem and how companies are using machine learning to drive results
• Preparing oneself for for what many industry leaders call a major shift in our society (Andrew Ng refers to AI as a "new electricity")
• Understanding basic concepts that often appear within research (it's helped me with understanding certain concepts that appear within Google Brain's Research)
• Growing as an individual and expanding your horizons (you might really enjoy machine learning!)
• When code works and data is produced, it's a very fulfilling, empowering feeling (even if it's a very humble result)

I spent a year taking online courses, reading books, and learning about learning (...as a machine). This post is the fruit borne of that labor -- it covers 17 machine learning resources (including online courses, books, guides, conference presentations, etc.) comprising the most affordable and popular machine learning resources on the web (through the lens of a complete beginner). I've also added a summary of "If I were to start over again, how I would approach it."

This article isn't about credit or degrees. It's about regular Joes and Joannas with an interest in machine learning, and who want to spend their learning time efficiently. Most of these resources will consume over 50 hours of commitment. Ain't nobody got time for a painful waste of a work week (especially when this is probably completed during your personal time). The goal here is for you to find the resource that best suits your learning style. I genuinely hope you find this research useful, and I encourage comments on which materials prove most helpful (especially ones not included)! #HumanLearningMachineLearning

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Executive summary:

Here's everything you need to know in a chart:

Machine Learning Resource	Time (hours)	Cost ($)	Year	Credibility	Code	Math	Enjoyability
Jason Maye's Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to	2	$0	'17
{ML} Recipes with Josh Gordon Playlist	2	$0	'16
Machine Learning Crash Course	15	$0	'18
OCDevel Machine Learning Guide Podcast	30	$0	'17-
Kaggle's Machine Learning Track (part 1)	6	$0	'17
Fast.ai (part 1)	70	$70*	'16
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	20	$25	'17
Udacity's Intro to Machine Learning (Kate/Sebastian)	60	$0	'15
Andrew Ng's Coursera Machine Learning	55	$0	'11
iPullRank Machine Learning Guide	3	$0	'17
Review Google PhD	2	$0	'17
Caltech Machine Learning on iTunes	27	$0	'12
Pattern Recognition & Machine Learning by Christopher Bishop	150	$75	'06				N/A
Machine Learning: Hands-on for Developers and Technical Professionals	15	$50	'15
Introduction to Machine Learning with Python: A Guide for Data Scientists	15	$25	'16	source https://moz.com/blog/learning-machine-learning