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5,063,219 Views • Sep 26, 2022 • Click to toggle off description
✏️ Kylie Ying developed this course. Check out her channel: youtube.com/c/YCubed
⭐️ Code and Resources ⭐️
🔗 Supervised learning (classification/MAGIC): colab.research.google.com/drive/16w3TDn_tAku17mum9…
🔗 Supervised learning (regression/bikes): colab.research.google.com/drive/1m3oQ9b0oYOT-DXEy0…
🔗 Unsupervised learning (seeds): colab.research.google.com/drive/1zw_6ZnFPCCh6mWDAd…
🔗 Dataets (add a note that for the bikes dataset, they may have to open the downloaded csv file and remove special characters)
🔗 MAGIC dataset: archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telesc…
🔗 Bikes dataset: archive.ics.uci.edu/ml/datasets/Seoul+Bike+Sharing…
🔗 Seeds/wheat dataset: archive.ics.uci.edu/ml/datasets/seeds
🏗 Google provided a grant to make this course possible.
⭐️ Contents ⭐️
⌨️ (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 to our Champion and Sponsor supporters:
👾 Raymond Odero
👾 Agustín Kussrow
👾 aldo ferretti
👾 Otis Morgan
👾 DeezMaster
--
Learn to code for free and get a developer job: www.freecodecamp.org/
Read hundreds of articles on programming: freecodecamp.org/news
Views : 5,063,219
Genre: Education
Date of upload: Sep 26, 2022 ^^
Rating : 4.887 (1,687/57,818 LTDR)
RYD date created : 2024-05-07T16:38:34.927598Z
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YouTube Comments - 1,176 Comments
Top Comments of this video!! :3
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!!!
588 |
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]
416 |
⌨ (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
421 |
Thanks for an amazingly simplified approach to ML 👍
428 |
NichesPanel likes this xD we all know that they isn't, but do you think models buy followers to appear on the internet?
1K |
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.
54 |
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!
86 |
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.
11 |
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')]
61 |
Kylie Ying is a gift to humanity
58 |
this is perfect! By far the best I´ve found out there, such a clear and complete explanation. Great teacher.
25 |
my 7th day - still not finished. Just so nice to see someone do ML work live! Thank you
79 |
falling asleep lands me in odd places
39 |
Absolutely brilliant. As mentioned in the intro Kylie is a true genius. god bless her
4 |
You are literally the best, I've been looking for a tutorial for three days and yours works
4 |
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!
14 |
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.
259 |
This is my first Course which I've completed from FCC, got a good understanding on ML now, Thank you !!
1 |
This is the best course ever on youtube about machine learning . She know the fundamental and know what to teach . ❤
1 |
@ImAnEmergency
1 year ago
I have no idea how my YouTube algorithm brought me here while I was sleeping but it made for some strange dreams
1.5K |