#39 Bayes Theorem - With Proof & Example |ML|
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- Опубліковано 8 лют 2025
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I see this theorem now and i no need any practice thank you my exam in with in 20 mins thanks a lot mam... Thanks 🙏🙏🌹🌹
Thank you for your lectures mam they are really helpful to prepare for sem exam....Hope you will be posting videos on unit 3,4,5 also. we have ML exam on 24 aug (JNTUH) syllabus
You are absolutely good teacher I like to watch your videos 🥰🥰🥰🥰🥰
Thank you so much you have explained the equations in a very simple way.
Thank you so much Mam for such a great explanation of all the concepts.. ✨🙌🏻🙏
You are amazing 💖
Tq mam mee valle iroju e ans attempt chesa🙏
Your explanation is just fabulous!!
UNIT - IV
Introduction to Cluster Analysis and Clustering Methods: The Clustering Task and the Requirements for
Cluster Analysis , Overview of Some Basic Clustering Methods:-, k-Medoids Clustering,
Density-Based Clustering: DBSCAN - Density-Based Clustering Based on Connected Regions with High Density,
Gaussian Mixture Model algorithm , Balance Iterative Reducing and Clustering using Hierarchies (BIRCH) ,
Affinity Propagation clustering algorithm,Mean-Shift clustering algorithm, ordering Points to Identify the
Clustering Structure (OPTICS) algorithm, Agglomerative Hierarchy clustering algorithm, Divisive Hierarchical ,
Measuring Clustering Goodness
Classification Model Evaluation and Selection: Sensitivity, Specificity, Positive Predictive Value, Negative
Predictive Value, Lift Curves and Gain Curves, ROC Curves, Misclassification Cost Adjustment to Reflect Real-
World Concerns, Decision Cost/Benefit Analysis
exam after 18 days
Suuppeerr madam
You're voice unconsiace
"I love you madam"
UNIT - III
Bayesian learning - Introduction, Bayes theorem, Bayes theorem and concept learning, Maximum
Likelihood and least squared error hypotheses, maximum likelihood hypotheses for predicting
probabilities, minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïve
Bayes classifier, an example: learning to classify text, Bayesian belief networks, the EM algorithm.
Computational learning theory - Introduction, probably learning an approximately correct hypothesis,
sample complexity for finite hypothesis space, sample complexity for infinite hypothesis spaces, the
mistake bound model of learning.
Instance-Based Learning- Introduction, k-nearest neighbour algorithm, locally weighted regression,
radial basis functions, case-based reasoning, remarks on lazy and eager learning.
Thanks maam.. you are the best!! I am having my DWDM exam tomorrow...
What's the result?
Mam please make next playlist on scripting languages and design and analysis of algorithms and fundamentals of iot
Do a playlist on java not for theory but for coding
Your explanation is very good mam but video quality is too bad please make it better so that the content can be visible clearly. For people to watch
Hi mam, your videos are very helpful. Thank you so much.
Please make videos on the subject Discrete Structures, it will be very helpful ❤
my respect to you ++++
My IV - 1 semister exams are from 17th december please make the classes on our subjects syllabus
Nice explanation 👌🏻
Is this complete playlist on ml?
Deep learning, cmrtc exams are from next week
You are saying is good
Thank you😊
if you don't mind mam please try to explain slowly I have been watching your viedos the whole day but everywhere I had felt like you are in a hurry to finish it off quickly so sometimes it is getting tough to catch your flow.
Thank you for good Explanation ❤️.Can u make videos on scripting language(JNTUH)
SREE DATTHA GROUP OF INSTITUTIONS
Exam on 28th.No need full syllabus.only 2 units or 3 units
supply padaledha
Today my exam 😅
My exam is on 22 Feb, 2023 and branch is AI&DS ,sub: probability and statistics
Tomorrow exam...
your voice iz choooooooo chweeeeeeeet uWu
thankyou so much ms
make videos on the subject introduction to data science jntuh syllabus
Hi mam,please explain Naive Bayes algorithm using numpy pandas and matplotlib
make videos on cryptography and network security'
Mam can you explain 4,5 units.bcoz those are small units.. and take less time mam.. please complete those units.plss
The other card symbol is CLOVER i guess.....and thanks for the video :✌
Software process project management (SPPM) e subject paina playlist cheyyandi
I have a lil confusion bayes classifier and bayes model are same or different
Thanku 👍👍👍👍👍 yar
Can u tell M1 and M3
Could you please make vedios for AI
4-2 semester .,C.S.E., jntuk university and also give some introduction about project
are you from data science background?
Mam can u please explain the occam's razor topic in unit4
This lectures is based on jntuh syllabus
can you please upload the scripting language classes
mam our exams are from3 june , can you make videos for design
analytics and algorithm
Conditional probability
Mam make playlist on embedded Systems mam
I Have MID Exam's On DEC 19th..
From VIT-AP
Mam if possible... Please make videos on the subject 'discrete mathematics' according to the r15 syllabus of JNTUA 🙏🙏🙏🙏🙏
I have exams by this month end...
Thanks mam
thanks
Mam please explain compiler design of jntuh r18
mam can you share the notes of 2nd unit
jntuh r18 3-1 exam on (wed)
Fabulous explanation...thanks
13th dec - statistical methods for engineers ----NALLA MALLA REDDY ENGINEERING COLLEGE
ok done
Today is my exam mam
Mam 4th my AI exm I am studying in North Campus delina baramulla I am form Kashmir
hello ma'am ,even I'm a jntuh student ,if unit 3 is easy compared to other units then fine else could you share the videos of unit 4 & 5 if at all possible since they are comparatively smaller than unit 3 is what I felt so that we'll be able to attempt the exam with ease on 24th aug .
just for reference
UNIT - III
Bayesian learning - Introduction, Bayes theorem, Bayes theorem and concept learning, Maximum
Likelihood and least squared error hypotheses, maximum likelihood hypotheses for predicting
probabilities, minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïve
Bayes classifier, an example: learning to classify text, Bayesian belief networks, the EM algorithm.
Computational learning theory - Introduction, probably learning an approximately correct hypothesis,
sample complexity for finite hypothesis space, sample complexity for infinite hypothesis spaces, the
mistake bound model of learning.
Instance-Based Learning- Introduction, k-nearest neighbour algorithm, locally weighted regression,
radial basis functions, case-based reasoning, remarks on lazy and eager learning.
UNIT- IV
Genetic Algorithms - Motivation, Genetic algorithms, an illustrative example, hypothesis space
search, genetic programming, models of evolution and learning, parallelizing genetic algorithms.
Learning Sets of Rules - Introduction, sequential covering algorithms, learning rule sets: summary,
learning First-Order rules, learning sets of First-Order rules: FOIL, Induction as inverted deduction,
inverting resolution.
Reinforcement Learning - Introduction, the learning task, Q-learning, non-deterministic, rewards and
actions, temporal difference learning, generalizing from examples, relationship to dynamic
programming.
UNIT - V
Analytical Learning-1- Introduction, learning with perfect domain theories: PROLOG-EBG, remarks
on explanation-based learning, explanation-based learning of search control knowledge.
Analytical Learning-2-Using prior knowledge to alter the search objective, using prior knowledge to
augment search operators.
Combining Inductive and Analytical Learning - Motivation, inductive-analytical approaches to
learning, using prior knowledge to initialize the hypothesis.
Bayesian learning methods video?
IF POSSSIBLE CAN U PREPARE BEFA FOR R18 SYALLABUS (2-2 SEM) ,IT IS AT THE TIME OF 25-27 OF AUG
Madam can you explain the introduction to data science subject in data science jntuh r18 regulation
Ay did you copy your logo from T-Force RAM?
Cosm mam
YOU HAVE RETURN THE NOTES RIGHT THAT NOTES CONVERTS TO PDF AND PROVIDE IT FOR FORTHER STUDENTS 😅
5/7/2023
From RVR JC college
We have exams from Jan 30 nd we are from JBREC jntuh college
Say important questions unit wise
tommorow is my exam mam😂😂
Ok
3rd Sem. MCA exam. Faridabad college of engineering and management, Haryana
mam ignou mca new please create vedios
june
Mam you have done mistake face/king is 12/4.but you have taken 4/4.
looking for this comment , thanx
P(F|K) means that king has already occurred and what's the probability of it being a face card, so it should be 1 as all kings are face cards.
Wow lol
thanks for the information
Those faces are of kings so it's 4/4
Global institute of mamangement sciences bangalore
07:46 Women.....jb direct hi cutt ho rha tha 52 then why we do fraction 😂🤣
6mins left for exam. So complete the plsylist😂
1hr before exam
Mam please please provide 4,5Units
Balaji institute of technology and science
notes I need all units of ml mam
Hello ma'am,
My college name is Sri Krishna Institute of Technology, Bangalore.
University:- VTU
7th Semester.
Exams are from January 2023
mam I have exam July 26th
January 8 2024 vemu it 3- 1
My colleage name annamarchy college tirupathi give the full course mam for 1 and 2 units
thank you akka, i write this corrector will fail me he is an assholwee
25th march 2023 Modern College
Gitam hyd
JNTU ANANTHAPUR
Anurag University
Exam in 3 hrs 😂
Hello mam. I am pursuing masters in USA and I have my machine learning exam within 15 days. So can you please let me know the Important concepts to prepare up for my examination ?
Today
dec end, inderprastha enginnering college(IPEC) (AKTU)
I am ignou student sub code mcs224 name artificial intelligence and learning, please help me imp topics to get pass Marks. Exam date 12 aug
Hindi please
Flat jntuh r18
Not available
@@hrithikchinnu6910 😂