Top three video resources for machine learning newbies


Several weeks ago, I wrote about practical advice from a Chief Data Scientist in my blog “From Aristotle to Pi: Practical advice from a chief data scientist.”  Now I want to offer my advice as a newbie trying to navigate through machine learning concepts and how to code them.

Over the past few years, I’ve been working on my Master of Science in Analytics and have had practice with machine learning algorithms. I initially thought that I could learn by just reading articles about the algorithms because that’s what I’ve always done when learning new concepts.  This time around, however, I’ve found that I need to connect with people via video and even watch them as they write the code or use a GUI to make the machine learning algorithms work. This helps me cross the bridge between theory and practical application.

I would like to thank many people who’ve been my teachers at 3:00 am while I was frantically searching for machine learning videos to complete my homework.  Here are my top three favorite and totally free video resources that I have consistently relied upon to advance my knowledge as a machine learning newbie and to help me pass my analytics classes.

1. Ask the Expert

Definitely find someone who is an expert with the tool that you are using to implement the machine learning algorithms, whether it’s a tool that only supports code or has a GUI front end. These experts know the tips and tricks and can save you big headaches. As a SAS Enterprise Miner newbie, I rely heavily on the SAS Ask the Expert series, especially the ones taught by super-duper SAS user Melodie Rush. Not only do I get to watch the recordings but I get the notes as well! So that is the best of both worlds for me. I was able to impress my fellow classmates with some of the tips that I learned. Plus I got an A on my assignment.

2. School of AI

Siraj Raval at the School of AI takes machine learning and makes it super entertaining. My personal favorite is a neural networks video, How to Make a Neural Network - Intro to Deep Learning #2, because he does a great job explaining how neural networks work and how the algorithm is constructed in the code. Sometimes you even get a catchy tune to sing along with, think “Back propagate to update weights”. You’ll find yourself watching his videos multiple times to make sure that you do not miss the references to modern culture. Plus, his motto is, “Solve it or I’ll die trying,” and he sometimes raps lyrics like this, “I was rockin blockchains back in 2014. Before Cuban thought chips meant currency.”

3. Free data mining tutorials

There are lots of how-to videos out there on machine learning. Chances are that you have your favorite data scientists that you go back to through their blogs or videos time and time again. I am lucky in that I’m surrounded by several brilliant data scientists at SAS. I don’t want to play favorites here and name names, so I’ll just point to the SAS Enterprise Miner Learn page as several of my favorites have contributed to these thoughtful machine learning videos.

This are just the top three machine learning video resources that I go to over and over again – but I know there are many more out there. Think back to when you were an analytics newbie and what helped you learn. What other machine learning videos should I be watching? Let me know your favorite video resources for machine learning by commenting below.


About Author

Susan Kahler

Global Product Marketing Manager for AI

Susan is a Global Product Marketing Manager for AI at SAS. She has her Ph.D. in Human Factors and Ergonomics, having used analytics to quantify and compare mental models of how humans learn complex operations. Throughout her well-rounded career, she has held roles in user centered design, product management, customer insights, consulting and operational risk. Susan recently completed her Master of Science in Analytics, focusing on healthcare analytics. She also holds a patent for a software navigation system to guide users through dynamically changing systems.

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