# Linear Regression – Machine Learning Fun and Easy

## 46 thoughts on “Linear Regression – Machine Learning Fun and Easy”

My EX is still dependent 😉

2. deltaPath says:

Where is the next part of this topic?

3. Eihoofd2 says:

I've always wondered about the relationship between pokemon and urban density.

4. Rahul pawar says:

Ex concept.. made me cry 😂

5. K. Jevon .D says:

thank you for using interesting examples like Pokemon vs urban density!

6. robertoloaiza95 says:

quick question! which one are the regressors?

7. Ahmed Hassan says:

where can i get the N??

8. gopi krishna says:

well explained>> Great

9. bonifacio_kid says:

Thank you!

I am in a machine learning class and its not my strong suit, but this video explains everything 4x better than my professor! Nice job! +1 Sub

11. Biman Phil says:

Nice examples are used, easy to understand

12. Choe Seonghun says:

By the way, Where is the explanation on the machine learning?

13. Manash Bhele says:

I don't want to be remembering my ex while studying. LOL

14. Mickelodian Surname says:

I had so much hoped that my exposure to math ended in 1992 after my finals, and I'd never see another formula that I actaully needed to use again. Yet, here I am in 2019 yet again sitting with a science calculator open on one screen, excel on another and listening to yet another asian guy twice as bright as I am…. Darn!

Here is a quick summary of linear regression:

– Linear regression is finding out the best linear relationship that describes some data you have.

– It is important to note, that you assume there is a linear relationship between your dependent and independent variables

– Once you make that assumption, you next need to figure out the specific linear relationship
– We know that the general form of a linear relationship looks like this: Ax + By = C

– We want to find a specific linear relationship, i.e. a specific set of A, B and C, such that, this linear relationship fits our data best.

– Let's expand on what we mean by "fits our data best"

– We know that once we get a linear relationship, that relationship allows us to predict our independent variable (y) given our dependent variable (x)

– We have some sample data (called the test data) where we allredy know the ys for given xs.

– What if for each of our sample data, we compare the known y and the y that we get if you use our specific linear relationship?

– So now, our goal has become: To find the specific linear relationship that will result in ys as close to your actual ys in your sample data as possible.

– If you have done that, then you say that you have found a "best" fit line.

– In the above example, we consider a situation where we only have one independent variable, but you could have many and the same concept will still apply.

-If you have many independent variables, the general form of a linear relationship will look like: Ax + By + Cz + … = F where A, B, C and F are parameters, you want to find values of A, B, C and F that fit your sample data best. See nothing changes if you add more independent variables!

The key thing to know about linear regression:
You assume there is a linear relationship between your data and you then find a specific linear relationship that best fits your data.

Thanks again for another very fun and informative video, I enjoy these a lot!

16. Raghavendra Prabhu says:

this one video was enough for me to subscribe your channel

17. Ganesh Beard says:

and amazing work! thanks alot

19. Eriol says:

if only my teachers taught this concept in more practical manner, I wouldn't have been so confused. I remember learning this things during junior high school but never understood what's the purpose of learning this

20. jakirajam Sunar says:

Really nice explanation on Linear regression

Thanks man !

22. giegie the_hill says:

thanksss….

23. Junior Yao says:

plz when come the programming part of it

24. Jerome Liwanag says:

Good thing you dont have indian accent .. I really understand a lot now .. Thanks

25. Bassel Kh says:

Why N = 6 instead of 7 in the equation? is it because it is sample population?

26. Rajiv Kumar says:

Brave, Great , no word to say — One of the finest way to teach people, while i dont have any mathematical background

27. PompiTube says:

blurb

Nice starting video… Especially remembering Y and X…,, 🤣

29. arpita behura says:

Great video! Where can I get the link of the linear regression implementation using python??

30. Ruben Rosales says:

why is this video so dark

31. Ragu Subedi says:

What are your a and b called?

32. Robin Xu says:

33. abhinaba hazarika says:

pokemon vs urban density !!!!

34. Phuc Coi says:

where is that equation from? I mean the R2 equation

35. Delbis Luciano says:

Thank you 🙌🏼🙌🏼

36. Vandel Jason Strypper says:

the first Indian youtuber I find it easy to understand

37. Sarwar Hayatt says:

can't find link to next lecture where you implement linear regression by scikit learn.

38. #Zeal says:

That ex girlfriend concept hurt bro!!😂

39. Regunathan PL says:

why is R^2 what does it mean what can we do with the R^2 value……?

40. K T. says:

How do you calculate ESTIMATION times with x & y variables. Lets say you have money transfer method a,b,c – debit,credit,bank account and you have a dataset with send/receive times x & y and u want to ESTIMATE the delivery time with the video's model. But x & y are in time frames not in definite numbers? like send at 14:03:01 on 01/03/16 , received 18:07:01 on 03/03/16 , should i convert to seconds or minutes or hours.min.sec. How do I do ESTIMATIONS on a dataset? Thank you

41. erikoui says:

Can this help me find da wey

42. joey says:

machine learning after all is maths!

43. sum1sw says:

Dear Sir, is there a video about regression for there is error in x and y ?

44. Sanket Gujar says:

Hey Nice work, can you please tell me how you create the graphics in this video

45. pavan kumar says:

Really good Explanation

46. Suraj Mundalik says:

You r d best!