From Scratch: How to Code Linear Regression in Python for Machine Learning Interviews!

Linear Regression in Python | Gradient Descend | Data Science Interview Machine Learning Interview

Correction:
8:40 gradients should be divided by "m" instead of "n".

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====================
Contents of this video:
====================
00:00 Linear Regression Overview
03:58 Gradient Descend
06:32 Linear Regression Implementation
10:48 Time and Space Complexity

[diegozpulido]

Hi Ema. Thank you very much for your videos. Thanks to them I got a Senior Data Scientist position at Facebook. I will forever thank you for your exceedingly good work.

[emma_ding]

Correction:
1. Thanks to Ji Xu - at 9:39, it should be divided by m instead of n.
2. Thanks to Hui Yi - at 9:39, beta_1 should be beta_other.

[XuJiBoY]

Very helpful video, clear and concise! One minor correction: at around 8:40 (updating the gradients in method compute_gradient()) I think you meant to divide by m instead of n.

[cccspwn]

One of the important interview questions that I've seen for linear regression is: "What are the linear regression assumptions"

[techedu]

Time complexity is assuming this is the code that would be used, which does not leverage GPU. With parallelization, a vector multiplication may be computed in constant time, reducing time complexity to O(M) from O(MN)

[jijyisme]

The explanation is very clear and concise! Thank you so much. Please keep it going.

[florachen]

I believe in the gradient descent step, it should be beta[i] -= (gradient_beta_other[i
] * learning_rare)
Since traversing down a slope requires taking the opposite sign of the computed the gradient

[jeoffleonora]

I like the step by step guide. And I learned a lot from your implementation.

[LuciaCasucci]

Thank you for the videos, Emma, I am on the very last round of Amazon and Microsoft for senior data scientist and I am finding your material excellent for prep and review!!! One thing I cannot understand what does the subscript j mean? how is it any different than i (slide at min 6 and 6 30)

[nanfengbb]

Thanks for posting a great video. Quick question, I noticed that you chose iteration = 100 and learning rate = 0.01 here. Is there a relationship between iteration and learning rate, e.g. iteration*learning=1?

[梅鹏飞-ge]

Bear with my rusty math ....
Calculating derivative of y over betai, at 6:27, how come it become Xji? It is making more sense if it is the derivative of y_hat over betai... consistent to the earlier slide at 2:50?

[jamiew]

Hi Ema, thanks for your video. I wonder how we can explain concepts like gradient decent, maximum likelihood, loglikelihood verbally in an interview?

[Han-veuh]

Thanks for sharing the implementation details. The explanation at 10:30 was hard to understand, specifically the reasoning that "if yhat>y, derror_dy is negative and thats why we add". Beta is being updated here, so the gradient is wrt B, so before reaching the error, B goes through yhat, then yhat contributes to E. That slide shows dE/dy, but where is dy/dB? It feels like a part of the chain rule was missing in that explanation, and directly jumped to "negative gradient" which includes dE/dy * dy/dB, but the latter term is not talked about in that slide.

[arieljiang]

Hi Emma thanks for the video, but n shouldn't be in the compute_gradient function. it should be m instead

[jessieshao]

Waiting for the new video for a week! It's coming!

[jiahuili]

is the gradient computed correctly at 6:20? the negative sign seems to be missing when doing derivatives of error wrt to yhat

[wongkitlongmarcus]

is this code too long for an interview?