Machine SEO

Tuesday, May 15, 2018

A Machine Learning Guide for Average Humans

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:

Machine Learning Resource	Time (hours)	Cost ($)	Year	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
Udacity's Machine Learning by Georgia Tech	96	$0	'15
Machine Learning Stanford iTunes by Andrew Ng	25	$0	'08	N/A

*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...)

Here's my suggested program:

1. Starting out (estimated 60 hours)

Start with shorter content targeting beginners. This will allow you to get the gist of what's going on with minimal time commitment.

Commit three hours to Jason Maye's Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to.
Commit two hours to watch Google's {ML} Recipes with Josh Gordon YouTube Playlist.
Sign up for Sam DeBrule's Machine Learnings newsletter.
Work through Google's Machine Learning Crash Course.
Start listening to OCDevel's Machine Learning Guide Podcast (skip episodes 1, 3, 16, 21, and 26) in your car, working out, and/or when using hands and eyes for other activities.
Commit two days to working through Kaggle's Machine Learning Track part 1.

2. Ready to commit (estimated 80 hours)

By this point, learners would understand their interest levels. Continue with content focused on applying relevant knowledge as fast as possible.

Commit to Fast.ai 10 hours per week, for 7 weeks. If you have a friend/mentor that can help you work through AWS setup, definitely lean on any support in installation (it's 100% the worst part of ML).
Acquire Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, and read the first two chapters immediately. Then use this as supplemental to the Fast.ai course.

3. Broadening your horizons (estimated 115 hours)

If you've made it through the last section and are still hungry for more knowledge, move on to broadening your horizons. Read content focused on teaching the breadth of machine learning -- building an intuition for what the algorithms are trying to accomplish (whether visual or mathematically).

Start watching videos and participating in Udacity's Intro to Machine Learning (by Sebastian Thrun and Katie Malone).
Work through Andrew Ng's Coursera Machine Learning course.

Your next steps

By this point, you will already have AWS running instances, a mathematical foundation, and an overarching view of machine learning. This is your jumping-off point to determine what you want to do.

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.); or applying machine learning to your own problems.

Why am I recommending these steps and resources?

I am not qualified to write an article on machine learning. I don't have a PhD. I took one statistics class in college, which marked the first moment I truly understood "fight or flight" reactions. And to top it off, my coding skills are lackluster (at their best, they're chunks of reverse-engineered code from Stack Overflow). Despite my many shortcomings, this piece had to be written by someone like me, an average person.

Statistically speaking, most of us are average (ah, the bell curve/Gaussian distribution always catches up to us). Since I'm not tied to any elitist sentiments, I can be real with you. 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. Click to expand each course for the full version with notes.

In-depth reviews of machine learning courses:

Starting out

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) ↓

Ready to commit

Fast.ai (part 1 of 2) ↓

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems ↓

Broadening your horizons

Udacity: Intro to Machine Learning (Kate/Sebastian) ↓

Andrew Ng's Coursera Machine Learning Course ↓

Additional machine learning opportunities

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. I'm genuinely not sure why I set my sights on this project, much less why I followed through with it. I saw Mike King give a session on Machine Learning. I was caught off guard, since I knew nothing on the topic. It gave me a pesky, insatiable curiosity itch. It started with one course and then spiraled out of control. Eventually it transformed into an idea: a review guide on the most affordable and popular machine learning resources on the web (through the lens of a complete beginner). Hopefully you found it useful, or at least somewhat interesting. Be sure to share your thoughts or questions in the comments!

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

Source: Moz Blog

Friday, May 11, 2018

Monitoring Featured Snippets - Whiteboard Friday

Posted by BritneyMuller

We've covered finding featured snippet opportunities. We've covered the process of targeting featured snippets you want to win. Now it's time for the third and final piece of the puzzle: how to monitor and measure the effectiveness of all your efforts thus far. In this episode of Whiteboard Friday, Britney shares three pro tips on how to make sure your featured snippet strategy is working.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today we are going over part three of our three-part series all about featured snippets. So part one was about how to discover those featured snippet opportunities, part two was about how to target those, and this final one is how to properly monitor and measure the effectiveness of your targeting.

So we'll jump right in. So there are a couple different steps and things you can do to go through this.

I. Manually resubmit URL and check SERP in incognito

First is just to manually resubmit a URL after you have tweaked that page to target that featured snippet. Super easy to do. All you do is go to Google and you type in "add URL to Google." You will see a box pop up where you can submit that URL. You can also go through Search Console and submit it manually there. But this just sort of helps Google to crawl it a little faster and hopefully get it reprioritized to, potentially, a featured snippet.

From there, you can start to check for the keyword in an incognito window. So, in Chrome, you go to File > New Incognito. It tends to be a little bit more unbiased than your regular browser page when you're doing a search. So this way, you'd start to get an idea of whether or not you're moving up in that search result. So this can be anywhere from, I kid you not, a couple of minutes to months.

So Google tends to test different featured snippets over a long period of time, but occasionally I've had experience and I know a lot of you watching have had different experiences where you submit that URL to Google and boom — you're in that featured snippet. So it really just depends, but you can keep an eye on things this way.

II. Track rankings for target keyword and Search Console data!

But you also want to keep in mind that you want to start also tracking for rankings for your target keyword as well as Search Console data. So what does that click-through rate look like? How are the impressions? Is there an upward trend in you trying to target that snippet?

So, in my test set, I have seen an average of around 80% increase in those keywords, just in rankings alone. So that's a good sign that we're improving these pages and hopefully helping to get us more featured snippets.

III. Check for other featured snippets

Then this last kind of pro tip here is to check for other instances of featured snippets. This is a really fun thing to do. So if you do just a basic search for "what are title tags," you're going to see Moz in the featured snippet. Then if you do "what are title tags" and then you do a -site:Moz.com, you're going to see another featured snippet that Google is pulling is from a different page, that is not on Moz.com. So really interesting to sort of evaluate the types of content that they are testing and pulling for featured snippets.

Another trick that you can do is to append this ampersand, &num=1, &num=2 and so forth. What this is doing is you put this at the end of your Google URL for a search. So, typically, you do a search for "what are title tags," and you're going to see Google.com/search/? that typical markup. You can do a close-up on this, and then you're just going to append it to pull in only three results, only two results, only four results, or else you can go longer and you can see if Google is pulling different featured snippets from that different quota of results. It's really, really interesting, and you start to see what they're testing and all that great stuff. So definitely play around with these two hacks right here.

Then lastly, you really just want to set the frequency of your monitoring to meet your needs. So hopefully, you have all of this information in a spreadsheet somewhere. You might have the keywords that you're targeting as well as are they successful yet, yes or no. What's the position? Is that going up or down?

Then you can start to prioritize. If you're doing hundreds, you're trying to target hundreds of featured snippets, maybe you check the really, really important ones once a week. Some of the others maybe are monthly checks.

From there, you really just need to keep track of, "Okay, well, what did I do to make that change? What was the improvement to that page to get it in the featured snippet?" That's where you also want to keep detailed notes on what's working for you and in your space and what's not.

So I hope this helps. I look forward to hearing all of your featured snippet targeting stories. I've gotten some really awesome emails and look forward to hearing more about your journey down below in the comments. Feel free to ask me any questions and I look forward to seeing you on our next edition of Whiteboard Friday. Thanks.

Video transcription by Speechpad.com