Machine Learning for Everybody – Full Course

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

falling asleep lands me in odd places

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!!!

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

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

Thanks for an amazingly simplified approach to ML 👍

It seems half of us are here after falling asleep

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.

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!

Kylie Ying is a gift to humanity

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.

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')]

It would be very beneficial for beginners to make Shallow, Deep and Convolutional Neural Networks from scratch. Because by doing so, they can learn many activations and their derivatives, forward propagation, and backward propagation. Along with, the dimensions of matrices and what is actually happening at each layer. Later on, they can shift to Tensorflow or Scikit when doing professional learning but I do advise to at least implement a neural network from scratch.

Enjoy these thorough, clear, visual explanations. She makes what we do accessible to beginners and a perfect refresh for seasoned users.

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

This is amazing. I'm a data analyst and had some formal training in machine learning, but my classes were really surface and "teach yourself" style. This is so much better. I also find it easier to listen to women, so that's a bonus lol

Repost from deep in the comments: ⌨ (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

18:10 As soon as you mentioned "Hot Dog or Not Hot Dog", it instantly reminded me of Jian Yang's classification model from the HBO comedy series Silicon Valley. 😂 But this course is very useful and easy to grasp for beginners like us. 👍

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