To support more videos like this, please check out my O'Reilly books. Essential Math for Data Science amzn.to/3Vihfhw Getting Started with SQL amzn.to/3KBudSY Access all my books, online trainings, and video courses on O'Reilly with a 10-day free trial! oreillymedia.pxf.io/1rJ1P6
I'm grateful that people like you carrying the work Grant started. One person can only do so much, but I'm sure people like you will revolutionise maths learning in the future with ever-growing topics covered.
I recently discovered that this is the Thomas Nield channel, and I must express my admiration. Your book, "Essential Math for Data Science," has been invaluable to my learning journey. Sir, your work is amazing, and I look forward to watching more of your videos. Please continue inspiring us with your expertise.
Thank you! It means a lot it helped you. And @boluwatifeadebowe1427 you can get the books here: Essential Math for Data Science amzn.to/3Vihfhw Getting Started with SQL amzn.to/3KBudSY You can also access all my books, live online trainings, and video courses on O'Reilly. oreillymedia.pxf.io/1rJ1P6
Hello, nice video ! how did you classifier in 2 perfects lines ? My hypothesis is that : Lets say you have 2 features : weight and height, you place them in a 2D plan. Then, you find a decision boundary, and give the decision boundary, you predicts all these points and then given the distance of each point and the decision boundary, you place them in the sigmoid function. Is it right ? If not, can you explain me briefly how we do that ? Because I'm but confused about what we optimize : In the video you explain that we optimize the sigmoid function in order to get the best accuracy. But in a 2d plan, how does this reflect, how do we see the line ? When we optimize the sigmoid function, does the line change or not ? Thanks in advance !
To support more videos like this, please check out my O'Reilly books.
Essential Math for Data Science
amzn.to/3Vihfhw
Getting Started with SQL
amzn.to/3KBudSY
Access all my books, online trainings, and video courses on O'Reilly with a 10-day free trial!
oreillymedia.pxf.io/1rJ1P6
I'm grateful that people like you carrying the work Grant started. One person can only do so much, but I'm sure people like you will revolutionise maths learning in the future with ever-growing topics covered.
I recently discovered that this is the Thomas Nield channel, and I must express my admiration. Your book, "Essential Math for Data Science," has been invaluable to my learning journey. Sir, your work is amazing, and I look forward to watching more of your videos. Please continue inspiring us with your expertise.
Good day, how can i get this book pls
Thank you! It means a lot it helped you. And @boluwatifeadebowe1427 you can get the books here:
Essential Math for Data Science
amzn.to/3Vihfhw
Getting Started with SQL
amzn.to/3KBudSY
You can also access all my books, live online trainings, and video courses on O'Reilly.
oreillymedia.pxf.io/1rJ1P6
Thank you! Using this to refresh the mechanics behind some methods for my data analytics course. Short, but powerful video.
This video could help you out too:
Another great video about logistic regression in JMP
th-cam.com/video/9yN_yjGAJZE/w-d-xo.htmlsi=jUwEZUDobBudE8AE
Your videos are so well made.
Very well made video! Reminds me of 3 blue 1 brown
Grant’s work was definitely an inspiration for this series! And thank you
@@3-minutedatascience also Manim is in use here am I right? Loved the video btw!
@@nfiu Yes, these videos use Manim
concise, clear and under 4 minutes. bravo and thanks for your work!
Underrated channel!
Thanks for this video! I like the visual graphics and the voice 😀
Another great video about logistic regression in JMP
th-cam.com/video/9yN_yjGAJZE/w-d-xo.htmlsi=jUwEZUDobBudE8AE
I love this video keep going :D
You could like this video too:
Another great video about logistic regression in JMP
th-cam.com/video/9yN_yjGAJZE/w-d-xo.htmlsi=jUwEZUDobBudE8AE
Great video! Thanks!
Upload more videos for the all Algorithms in machine learning and deep learning
Amazing, this is such a clear and concise video. What do you use for your animations?
Manim CE, just Google it : )
Thank you very much.
I'd like to see a video of polynomial regression from you :)
bravo - well done
Hello, nice video ! how did you classifier in 2 perfects lines ? My hypothesis is that : Lets say you have 2 features : weight and height, you place them in a 2D plan. Then, you find a decision boundary, and give the decision boundary, you predicts all these points and then given the distance of each point and the decision boundary, you place them in the sigmoid function. Is it right ?
If not, can you explain me briefly how we do that ? Because I'm but confused about what we optimize : In the video you explain that we optimize the sigmoid function in order to get the best accuracy. But in a 2d plan, how does this reflect, how do we see the line ? When we optimize the sigmoid function, does the line change or not ?
Thanks in advance !
when are we getting the maximum likelihood video
amazing video, Thank you very much
tnx
😊😊😊
☯️🙏
Ｐｒⓞм𝕠𝕤𝐌
bruh wasted the first 15 seconds
😂
statquest is better explainer
I liked this better
Both r great
Why would u demotivate a guy trying to teach others