Machine Learning for Everybody – Full Course

I've been trying to learn ML for quite awhile but could never really grasp the algorithim. She explains how the formula comes about and why is it used in the classification or regression so well. My god. Thumbs up for sensei Kylie and free code camp!!!

For anyone getting an error related to converting a list to a float, the model.evaluate is actually returning a list. She has the correction in the code at around 2:05:51, but she doesn't explicitly mention the correction. You just grab the first value in the list (which is why she puts [0]). So change the line where you obtain the val_loss to: val_loss = model.evaluate(X_valid, y_valid)[0]

⌨ (0:00:00) Intro ⌨ (0:00:58) Data/Colab Intro ⌨ (0:08:45) Intro to Machine Learning ⌨ (0:12:26) Features ⌨ (0:17:23) Classification/Regression ⌨ (0:19:57) Training Model ⌨ (0:30:57) Preparing Data ⌨ (0:44:43) K-Nearest Neighbors ⌨ (0:52:42) KNN Implementation ⌨ (1:08:43) Naive Bayes ⌨ (1:17:30) Naive Bayes Implementation ⌨ (1:19:22) Logistic Regression ⌨ (1:27:56) Log Regression Implementation ⌨ (1:29:13) Support Vector Machine ⌨ (1:37:54) SVM Implementation ⌨ (1:39:44) Neural Networks ⌨ (1:47:57) Tensorflow ⌨ (1:49:50) Classification NN using Tensorflow ⌨ (2:10:12) Linear Regression ⌨ (2:34:54) Lin Regression Implementation ⌨ (2:57:44) Lin Regression using a Neuron ⌨ (3:00:15) Regression NN using Tensorflow ⌨ (3:13:13) K-Means Clustering ⌨ (3:23:46) Principal Component Analysis ⌨ (3:33:54) K-Means and PCA Implementations

NichesPanel likes this xD we all know that they isn't, but do you think models buy followers to appear on the internet?

Thanks for an amazingly simplified approach to ML 👍

Kylie Ying is a gift to humanity

I have to agree with those calling this tutorial too hard. I am a professional developer studying Cyber-Sec at the Master's level and found the first hour of the tutorial to be so intimidating that I had to go and learn Python again, just to boost my confidence. I followed it by getting a tutorial on Pandas as well as Numpy, those helped. I came back and realized that, while this is a really good tutorial, it isn't beginner-friendly at all. The kind of stuff Kylie accomplishes in a single line needs multiple lines from me and many more minutes to understand what's going on. As advice to all the newbies, don't be intimidated, try taking the Python basics, Pandas and Numpy courses before attempting this tutorial, perhaps watch the first hour to see what's required and come back.

Yesterday I click on a video called 'learning phyton for Beginners'. Today youtube's algorithm sent this video. I was so confuse but somehow listen to it and when I feel I understand something from this explanation, it makes me excited. A genius can make someone understand complicated things, I am very grateful.

I have no idea how my YouTube algorithm brought me here while I was sleeping but it made for some strange dreams

You are literally the best, I've been looking for a tutorial for three days and yours works

I can tell you are one hell of a singer. The vocals in every explanation is like an instrument.

this is perfect! By far the best I´ve found out there, such a clear and complete explanation. Great teacher.

Absolutely brilliant. As mentioned in the intro Kylie is a true genius. god bless her

Her voice and way of teaching is so soothing. I fell asleep listening to her and I am gonna watch this every night.

falling asleep lands me in odd places

This is my first Course which I've completed from FCC, got a good understanding on ML now, Thank you !!

Kylie is such a great teacher and obviously not only understands but applies these topics in the real world. What a great combination, thanks for the course!

I cannot even begin to describe how useful this video was for me. Endless thanks for the priceless knowledge Kylie and freeCodeCamp provided. This video seriously gave me an edge when it came to machine learning. It organized all the abstract concepts I have been hearing about while also describing them perfectly. I have learned SO much. Thank you, thank you, thank you!

If you're getting an error about comparing a list to a float. Changing the "least_val_loss" variable to a list with two infinite floats will fix it. Like this: least_val_loss = [float('inf'), float('inf')]

my 7th day - still not finished. Just so nice to see someone do ML work live! Thank you