# LDA (Linear Discriminant Analysis) In Python – ML From Scratch 14 – Python Tutorial

///LDA (Linear Discriminant Analysis) In Python – ML From Scratch 14 – Python Tutorial

## LDA (Linear Discriminant Analysis) In Python – ML From Scratch 14 – Python Tutorial

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In this Machine Learning from Scratch Tutorial, we are going to implement the LDA algorithm using only built-in Python modules and numpy. LDA (Linear Discriminant Analysis) is a feature reduction technique and a common preprocessing step in machine learning pipelines. We will learn about the concept and the math behind this popular ML algorithm, and how to implement it in Python.

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By |2020-12-31T11:35:40+00:00December 31st, 2020|Python Video Tutorials|17 Comments

1. Houda CHAKIR December 31, 2020 at 11:35 am - Reply

THANK YOU SO MUCH

2. shafil hosain December 31, 2020 at 11:35 am - Reply

excellent explanation, Many thanks

3. rohan devaki December 31, 2020 at 11:35 am - Reply

you are nicely reading the ppt, very good, 😒
try to explain it , dont just read it.

4. 최진영 December 31, 2020 at 11:35 am - Reply

When I use this code to sklearn.datasets.load_digits, it occurs singular matrix error when calculate np.linalg.inv(SW).
Why this occurs?

5. Sujal Bhagat December 31, 2020 at 11:35 am - Reply

It is great video. Can you send me the code into email: sujalbhagat97@gmail.com

6. Mostafa Shahhosseini December 31, 2020 at 11:35 am - Reply

I didn't understand what's the mathematical references of your work. Why did you use that transformation? is it the same with SVD? please note the reference or some keywords for me to study more about the mathematical stuff related to the transformation you made use of that. thanks

7. Kurnia Adi C December 31, 2020 at 11:35 am - Reply

permission to learn sir

8. prashant sharma December 31, 2020 at 11:35 am - Reply

hi getting this error,. still LDA is able to reduce the dimensions to 2
"Value 'eigenvectors' is unsubscriptable"
and why cant we sort the idxs with eign vectors argument?

update: solved that issue by converting the eignvectors to numpy array… thankx to you , my programing skills are getting better.

9. Ayush Koul December 31, 2020 at 11:35 am - Reply

Are you gonna do a playlist for NN from scratch?

10. Chenyong Miao December 31, 2020 at 11:35 am - Reply

Finally, finished learning your 14 ML videos. Learned a lot about ML algorithms and numpy skills. Thanks a lot !

11. Anudeep December 31, 2020 at 11:35 am - Reply

One of the recent interesting works in DL is Batch Normalization.
I tried a lot, understanding online, how to implement batch Norm from scratch, but couldn't understand.
My main problem is understanding Backpropagation in Batch Norm. Can you do a video on it, it'll be very helpful or at least share any resources (if you have any) for backpropagation in Batch Norm.

12. John Parker December 31, 2020 at 11:35 am - Reply

Great Work!
PS: Please make video on on implementing CNN algorithm. It will mean a lot of help for academic students

13. mahta zarean December 31, 2020 at 11:35 am - Reply

hi great job .can you also make a viedo on EM(expectation maximization)?tnx alot

14. Rajganesh Pandurangan December 31, 2020 at 11:35 am - Reply

of topic. what are your thoughts on Andrew-ng DL course? some feedback says it's hard to understand the Math. if you know the high school math basics, is it possible to follow that course?

15. Daniel Weikert December 31, 2020 at 11:35 am - Reply

The biggest thing I am struggling with are the shapes for the neural networks, data feeding in the e.g Embedding layer and reshapings. Is this a topic you can recommend a good learning material or even dive into in one of your videos?
Thanks

16. Biranchi Narayan Nayak December 31, 2020 at 11:35 am - Reply

Excellent tutorial on PCA and LDA

17. Huy Nguyễn December 31, 2020 at 11:35 am - Reply

first again!