# Scikit-Learn Course – Machine Learning in Python Tutorial

///Scikit-Learn Course – Machine Learning in Python Tutorial

## Scikit-Learn Course – Machine Learning in Python Tutorial

Scikit-learn is a free software machine learning library for the Python programming language. Learn about machine learning using scikit-learn in this full course.

💻 Code:
🔗 Scikit-learn website:
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⭐️ Course Contents ⭐️

Chapter 1 – Getting Started with Machine Learning
⌨️ (0:00) Introduction
⌨️ (0:22) Installing SKlearn
⌨️ (3:37) Plot a Graph
⌨️ (7:33) Features and Labels_1
⌨️ (11:45) Save and Open a Model

Chapter 2 – Taking a look at some machine learning algorithms
⌨️ (13:47) Classification
⌨️ (17:28) Train Test Split
⌨️ (25:31) What is KNN
⌨️ (33:48) KNN Example
⌨️ (43:54) SVM Explained
⌨️ (51:11) SVM Example
⌨️ (57:46) Linear regression
⌨️ (1:07:49) Logistic vs linear regression
⌨️ (1:23:12) Kmeans and the math beind it
⌨️ (1:31:08) KMeans Example

Chapter 3 – Artificial Intelligence and the science behind It
⌨️ (1:42:02) Neural Network
⌨️ (1:56:03) Overfitting and Underfitting
⌨️ (2:03:05) Backpropagation
⌨️ (2:18:16) Cost Function and Gradient Descent
⌨️ (2:26:24) CNN
⌨️ (2:31:46) Handwritten Digits Recognizer

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By |2020-09-23T09:39:08+00:00September 23rd, 2020|Python Video Tutorials|33 Comments

1. The DL Academy September 23, 2020 at 9:39 am - Reply

It was a great pleasure working with freecodecamp to bring you this content!!! Freecodecamp is probably one of the best communities out there for aspiring programmers, if not the best!! If you liked this video, you'll love our YouTube content even more – TheDLAcademy

2. agoss flores September 23, 2020 at 9:39 am - Reply

holaaa lindo video desearía que permanezcan haciendo este metarial¡¡¡!!! Voy a seguir mirando sus creaciones. Les mando un beso nos estamos viendo ❤️👍🏽

3. Yuwen Chen September 23, 2020 at 9:39 am - Reply

Great video. Many thanks

4. matias torres September 23, 2020 at 9:39 am - Reply

Thanks

5. Rin Okumura September 23, 2020 at 9:39 am - Reply

Thank you very much 🙂

6. Boss is Right September 23, 2020 at 9:39 am - Reply

yo boiiii youreee godddd

7. didier Leprince September 23, 2020 at 9:39 am - Reply

Merci 😊

2h Abdul vin can Nd ah

9. McMIKEGLIVe September 23, 2020 at 9:39 am - Reply

Free code camp is the best platform ever but you,,you are the worst lecturer ever😂😂..am in 15th min and i still don't know what am learning and you are there with a complete model(i donno what this is) which you are showing me how to save lol..no offense though..i just can't understand anything you are teaching.

10. Sai Balaji September 23, 2020 at 9:39 am - Reply

Why we should find transpose of X in linear regression??

21

12. AcousticCover September 23, 2020 at 9:39 am - Reply

Man the fact that you guys make these lessons and then upload them free for all is just very admirable. Grateful for Freecodecamps.

13. Светлана Маковка September 23, 2020 at 9:39 am - Reply

The worst tutorial I have ever seen

14. Arun kumar Acharya September 23, 2020 at 9:39 am - Reply

At 49:00, it gets more textual with little insights from the speaker and adds no value! Sorry to say, but this video just gives information rather than explaining the science and logic behind it

15. Transportia September 23, 2020 at 9:39 am - Reply

Good content and helpful learn-by-coding approach. The sound quality could be better–I know a lot of programmers love their mechanical keyboards but it's hella distracting for tutorial purposes.

16. Vasanth Fransua September 23, 2020 at 9:39 am - Reply

So instead of using 'from sklearn.externals import joblib' use 'import joblib' directly as the former brings up few errors. 🙂 Otherwise I really liked this, great content.

17. Jayson Faulds September 23, 2020 at 9:39 am - Reply

I'm about 36 minutes in and they are not using LabelEncoder() correctly. LabelEncoder converts to 0, 1, 2, etc. by alphabetical order. Using the car data, high will equal 0, low will equal 1, med will equal 2, and vhigh will equal 3. This does not make any sense

Edit 1: For the SVM section, he has continuous features but he doesn't standardize them to a uniform scale. He should probably do that because it could skew the results

18. marveso September 23, 2020 at 9:39 am - Reply

Atrocious sound quality, sound is out of sync, your ability to explain something is also horrendous. You should be ashamed of these videos.

19. outcompete September 23, 2020 at 9:39 am - Reply

Check out my tutorial on sklearn, I go over preprocessing, scaling the data, metrics using classification reports and confusion matrix and much more!

20. Lakshya Bhardwaj September 23, 2020 at 9:39 am - Reply

a good tutorial as always, but pointer work was bad

21. jaymzleutz September 23, 2020 at 9:39 am - Reply

First and foremost: THANK YOU SO MUCH for this video. Undoubtedly, a honourably initiative. I would like to understand the bench_k_means at the end of K-Means example video, though. It seems to me that the function didn't work at all. Where did the "1" name come from? Is it irrelevant? We can pass any name as wanted by parameter? Thanks!

22. Antonio Innocente September 23, 2020 at 9:39 am - Reply

36:50 The comments inside LabelEncoder() say that it should only be used to encode target values and should NOT be used to encode the input X

23. Syed Yawar Ali Shah September 23, 2020 at 9:39 am - Reply

after 30 mins. I understand nothing at all.

24. Saurabh Mishra September 23, 2020 at 9:39 am - Reply

Prerequisites ??

25. Christer K September 23, 2020 at 9:39 am - Reply

god forbid you use the same sound / mic setting for the different parts. it would be a shame if the sound was the same all over.

26. akshay s September 23, 2020 at 9:39 am - Reply

i wish i had found about code camp when it started . it would have changed my life.

27. Amber S R P September 23, 2020 at 9:39 am - Reply

I was wondering, If I use Colab, I’d need not to install python on my laptop, right?

28. Apoorv Mote September 23, 2020 at 9:39 am - Reply

I understand why its free. Its absolute nightmare. And I have already taken 2 paid courses on SKLearn. There is huge difference between paid and free content.

29. Abhay Tiwari September 23, 2020 at 9:39 am - Reply

Thanks for Uploading this , But does this cover everything in Scikit-learn or just the important or useful features?

30. Jerzy Słomkowski September 23, 2020 at 9:39 am - Reply

Overall this is good course however sci-kit learn is not only about algorithms. Ther's a ton of preprocessing modules, Pipelines, Model selection, and evaluation methods that I feel should be addressed.

31. Luis Miguel Rodriguez September 23, 2020 at 9:39 am - Reply

It would be great if this course had a translation

32. MrHihihihhi September 23, 2020 at 9:39 am - Reply

Hi, towards the end of minute 43, would it not be better to specify the 'test' variables only when comparing predictions? As in much of the random variable 'a' would simply otherwise just be referring to data which is already trained and thus for this data 'predictions' will always come out correct right?

33. Chknise September 23, 2020 at 9:39 am - Reply