MLT Quiz-1 PYQ | Sept 23 | step-wise explained |
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- Опубликовано: 27 июн 2024
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In this video, I have created a detailed, step-by-step explanation of PYQ of the Machine Learning Technique (MLT) Quiz-1 from the SEPTEMBER 23 term for the IITM BS Degree program. If you're preparing for your upcoming exams or just looking to strengthen your understanding of machine learning concepts, this video is perfect for you.
📚 What You'll Learn:
A comprehensive breakdown of each question from the quiz.
Detailed explanations and solutions.
Key concepts and techniques in machine learning.
Tips and strategies to tackle similar problems.
👨🏫 About the IITM BS Degree Program:
The IIT Madras BS Degree is a unique online program designed to provide a solid foundation in Data Science and Programming. It offers flexible learning paths and is a great opportunity for anyone looking to advance their knowledge and career in technology.
✍️ Comments:
Feel free to leave any questions or feedback in the comments section below. I love hearing from you and will do my best to respond to each comment.
Happy learning! 🚀
00:05 Solving MLT Quiz with 11 questions and 50 marks
02:54 Understanding how data points converge and how to determine cluster means.
08:11 Explanation of Bernoulli distribution in MLT
10:41 Solving for eigenvalues and eigenvectors using common factor method
15:52 Estimation of parameters using beta distribution
18:24 Calculating posterior mean from beta distribution
22:36 Explanation on how to calculate maximum distance in data points
24:47 Discussing cluster means and probability calculations.
30:15 Understanding feature relationships in MLT
33:02 Understanding the concept of a perfect net
Great job 👏👏
25:55 TA ne confirm kara hain discourse pe ki (13,13) sahi hain
Thanku for confirmation
bhaiya last question correlation 0 kese aayi?
This is rule , in low corelation points are widely seperated.
Refer chatgpt for more detail
@@ashishmaurya7157 high variance means low coorelation?
MLT week 4 activity questions kab aenge bhaiya?
Tomorrow
I have a doubt in Manhattan distance formula isn"t it |x2-x1| + |y2-y1|
@@Jymofxyz yes
I did only 1st half in the video,but I have mentioned that how to calculate it.
@@ashishmaurya7157 how?
iitm disliked this video due to last question
Do they also watch😄😄