A Machine Learning Guide for Average Humans

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). 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). I genuinely hope you find this research useful, and I encourage comments on which materials prove most helpful (especially ones not included)! Starting out (estimated 60 hours) Start with shorter content targeting beginners. Ready to commit (estimated 80 hours) By this point, learners would understand their interest levels. Your next steps By this point, you will already have AWS running instances, a mathematical foundation, and an overarching view of machine learning. You should be able to determine your next step based on your interest, whether it's entering Kaggle competitions; doing Fast.ai part two; diving deep into the mathematics with Pattern Recognition & Machine Learning by Christopher Bishop; giving Andrew Ng's newer Deeplearning.ai course on Coursera; learning more about specific tech stacks (TensorFlow, Scikit-Learn, Keras, Pandas, Numpy, etc. Below contains a high-level summary of my reviews on all of the classes I took, along with a plan for how I would approach learning machine learning if I could start over. In-depth reviews of machine learning courses: Jason Maye's Machine Learning 101 slidedeck: 2 years of head-banging, so you don't have to ↓ {ML} Recipes with Josh Gordon ↓ Google's Machine Learning Crash Course with TensorFlow APIs ↓ OCDevel's Machine Learning Guide Podcast ↓ Kaggle Machine Learning Track (Lesson 1) ↓ Fast.ai (part 1 of 2) ↓ Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems ↓ Udacity: Intro to Machine Learning (Kate/Sebastian) ↓ Andrew Ng's Coursera Machine Learning Course ↓ iPullRank Machine Learning Guide ↓ Review Google PhD ↓ Caltech Machine Learning iTunes ↓ "Pattern Recognition & Machine Learning" by Christopher Bishop ↓ Machine Learning: Hands-on for Developers and Technical Professionals ↓ Introduction to Machine Learning with Python: A Guide for Data Scientists ↓ Udacity: Machine Learning by Georgia Tech ↓ Andrew Ng's Stanford's Machine Learning iTunes ↓ Motivations and inspiration If you're wondering why I spent a year doing this, then I'm with you.

Author: Alexis Sanders / Source: Moz

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

Executive summary:

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

*Free, but there is the cost of running an AWS EC2 instance (~$70 when I finished, but I did tinker a ton and made a Rick and Morty script generator, which I ran many epochs [rounds] of…)