5 minutes of Machine Learning : Google’s ML Crash Course Review [Day 1]

As part of my journey to teach myself machine learning, I have decided to take on Google’s Machine Learning Crash Course, available for free to the public!

My goal with this series: create a series of posts that takes the main concepts I found useful/ground-breaking/awe-inspiring in my journey and communicate them to you in five minutes of reading! This first post will focus on whether taking the ML Crash Course is for you. This post will change over time as I continue to sluff through the course, so come back for the most updated impressions and feedback I have.

First disclaimer: While this course is oftentimes advertised as good for those with no machine learning background (which it technically kind of is…) I would not recommend starting your journey with this course if you have had zero exposure to the overarching concepts of the field of artificial intelligence and machine learning. See the pre-requisites for the course here as well- yes, that course in Linear Algebra in high school that you took forever ago actually matters now!

I am a few modules into the course as of now, and here are my large takeaways, followed by some notes I thought were significant that I learned from the course:

1. If you want to truly understand how Tensorflow models work inside out, all the way down to the bare bones math, this course is for you.

2. If you are looking to actually build code and perform Tensorflow development, this course is for you.

3. A lot of the common terms used to define topics in machine learning… have two flavors of definitions. Some are the generalized versions used to present overall ML concepts- the other flavor is what each term actually means in mathematical TensorFlow concepts.

There is your tidbit for now. Go to this next article for the first actual five minutes of learning… about machine learning from the Crash Course.

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