Ridge Regression Part 3 | Gradient Descent | Regularized Linear Models

In the third installment of our series, we delve into Ridge Regression with a focus on Gradient Descent. Explore how this optimization technique plays a crucial role in implementing Ridge Regression, a powerful form of regularized linear models.
Code : github.com/campusx-official/1...
Matrix Differentiation : www.gatsby.ucl.ac.uk/teaching/...
Videos to watch:
• Multiple Linear Regres...
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[@krupal_patel]

Best teacher with 0 haters.. best channel for ml..ds, dl, ai

[@shivankjat5173]

bro appko bhi khud se apply karne me dikkat aati he kya me khud se apply nhi kar pa raha hu

[@kamrezgaming6916]

Appreciate your effort, I loved way you expand

[@BharatJain-bl5gw]

Best channel for Data science aspirants ❤️❤️ GBU👍👍

[@balrajprajesh6473]

Thanks for this, sir! you are great.

[@ParthivShah]

Thank You Sir.

[@stevegabrial1106]

thanks for ridge regression, see u all tomorrow.

[@devendrasharma5567]

acha se samjh me aa gya, mai pareshan the ka loss fun +reg. term me min of w kaise nikala jata hai jo ki aaj mujjhe samjh me aa gya

[@rohitdahiya6697]

why there is no learning rate hyperparameter in scikit-learn Ridge/lasso/Elasticnet . As it has a hyperparameter called max_iteration that means it uses gradient descent but still there is no learning rate present in hyperparameters . if anyone knows please help me out with it.

[@prathameshkhatu3182]

Nitish Sir @CampusX @6:42 you multiplied right side by 1/2 but not the left side...I think it may not be mathematically correct...can you or anyone explain the mathematics maybe I am missing something...? also @12.35 2WtXtY ka derivative would be 4 XtY isn't it sir?

[@Deepakkryadav-nx1ck]

yes you are nice observation ....

[@hossain9410]

How can i use regularization with supervised machine learning algorithmic??

[@saurabhbarasiya4721]

Thanks

[@katw434]

Thanks Sir

[@humerashaikh3618]

Sir pls explain svm regression

[@saumyashah6622]

Sir, I am very much happy that we are learning everything regularly. But sir I had a doubt. I have identified some niche topics in ML which are on sklearn API documentation. I don't know if they are important or not. Are we going to cover these:
1. Unsupervised learning
2. Manifold Learning
3. Reinforcement Learning
4. Discriminant Analysis
5. Gaussian Process
6. Multioutput, Multilabel classification
7. Random projection
8. Semi supervised learning

[@campusx-official]

Planning to cover 1,4,6 and 8. Will create separate playlist for 2 and 3

[@saumyashah6622]

@@campusx-official thanks for your reply.

[@stevegabrial1106]

@@campusx-official Plz also create playlist for GNN, thx.

[@ameerazam3269]

@@campusx-official 8.34 (Y.T*X*W) but you write as (Y.T*W*X) please ...

[@kindaeasy9797]

Y^tXW hoga

[@kindaeasy9797]

Left side pr 2 balance kr lete sir

[@aayushbisht9666]

sir whats difference btw L=(ypred - y)^2 and L=(y-ypred)^2
in multipe linear regression its 1st one so we got (yT -(XB)T)(y-XB)
but in Ridge Regression its L=(y-ypred)^2 which results (XB-y)T (XB -y)
does it matter or just sign changes

[@abhasmalguri2905]

bro it is matrix multiplication, so we can't change order anytime unless we know they commute

[@shreyasmhatre9393]

L= ( yi - ŷi ) ²
In matrix from
L= ( y - Xw )ᵀ ( y - Xw )
L= ( y - Xw )ᵀ ( y - Xw ) + || w || ²
L= ( y - Xw )ᵀ ( y - Xw) + λ wᵀw
L= ( yᵀ - wᵀ Xᵀ )( y - Xw) + λ wᵀw
L= yᵀ y - wᵀ Xᵀ y - yᵀXw + wᵀXᵀXw + λ wᵀw
As he told wᵀXᵀy and yᵀXw both are same
L= yᵀy - 2(wᵀXᵀy) + wᵀXᵀXw + λ wᵀw
this is same eqn he got

Eg =
(A-B)(C-D) = AC - BC -AD + BD ----- 1
(B-A)(D-C) = BD - AD - BC + AC re-arrange (AC - BC -AD + BD) ---- 2
Both eqn found out to be same