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Gurukul Wisdom
India
เข้าร่วมเมื่อ 5 ส.ค. 2018
#72 | Reinforcement Learning | Machine Learning
In this video, we will learn about, Reinforcement Learning In Machine Learning.
มุมมอง: 55
วีดีโอ
#71 | Apriori | Machine Learning
มุมมอง 222 ปีที่แล้ว
In this video, we will learn about Apriori in Machine Learning.
#70 | Association Rule Learning | Machine Learning
มุมมอง 1152 ปีที่แล้ว
In this video, we will learn about, Association Rule Learning in Machine Learning.
#69 | Cosine Similarity | Machine Learning
มุมมอง 1472 ปีที่แล้ว
In this video, we will learn about Cosine Similarity in Machine Learning.
#68 | Hybrid Filtering | Machine Learning
มุมมอง 2952 ปีที่แล้ว
In this video, we will learn about Hybrid Filtering in Machine Learning.
#67 | Collaborative Filtering | Machine Learning
มุมมอง 92 ปีที่แล้ว
In this video we will learn about, Collaborative Filtering in Machine Learning.
#66 | Content Based Filtering | Machine Learning
มุมมอง 122 ปีที่แล้ว
In this video we will learn about, Content-Based Filtering In Machine Learning.
#65 | Principle Component Analysis | Machine Learning
มุมมอง 102 ปีที่แล้ว
In this video, we will learn about Principle Component Analysis in Machine Learning.
#64 | Dimensionality Reduction | Machine Learning
มุมมอง 172 ปีที่แล้ว
In this video, we will learn about, Dimensionality Reduction in Machine Learning.
#63 | K-Means Clustering | Machine Learning
มุมมอง 162 ปีที่แล้ว
In this video, we will learn about, K-Means Clustering in Machine Learning.
#62 | Clustering In Machine Learning | Machine Learning
มุมมอง 82 ปีที่แล้ว
In this video we will learn about, Clustering In Machine Learning.
#61 | ROC - AUC Curve | Machine Learning
มุมมอง 512 ปีที่แล้ว
In this video, we will learn about, ROC- AUC Curve in Machine Learning.
#60 | Recall | Machine Learning
มุมมอง 42 ปีที่แล้ว
In this video, we will learn about, Recall in Machine Learning.
#59 | Precision | Machine Learning
มุมมอง 32 ปีที่แล้ว
In this video we will learn about, Precision in Machine Learning.
#58 | False Positive Rate | Machine Learning
มุมมอง 252 ปีที่แล้ว
In this video, we will learn about, False Positive Rates in Machine Learning.
#55 | Accuracy | Machine Learning | Machine Learning
มุมมอง 42 ปีที่แล้ว
#55 | Accuracy | Machine Learning | Machine Learning
#54 | Confusion Matrix | Machine Learning
มุมมอง 402 ปีที่แล้ว
#54 | Confusion Matrix | Machine Learning
#53 | Stratified Cross Validation | Machine Learning
มุมมอง 6862 ปีที่แล้ว
#53 | Stratified Cross Validation | Machine Learning
#52 | K-Fold Cross-Validation | Machine Learning
มุมมอง 422 ปีที่แล้ว
#52 | K-Fold Cross-Validation | Machine Learning
#51 | Support Vector Machine | Machine Learning
มุมมอง 52 ปีที่แล้ว
#51 | Support Vector Machine | Machine Learning
#50 | Random Forest Classifier | Machine Learning
มุมมอง 302 ปีที่แล้ว
#50 | Random Forest Classifier | Machine Learning
#48 | Information Gain | Machine Learning
มุมมอง 62 ปีที่แล้ว
#48 | Information Gain | Machine Learning
#46 | Decision Tree Classification | Machine Learning
มุมมอง 72 ปีที่แล้ว
#46 | Decision Tree Classification | Machine Learning
#45 | Logistic Regression | Machine Learning
มุมมอง 92 ปีที่แล้ว
#45 | Logistic Regression | Machine Learning
#43 | R Square & Adjusted R Square | Machine Learning
มุมมอง 282 ปีที่แล้ว
#43 | R Square & Adjusted R Square | Machine Learning
bhariable
Great video, straight to the point, thank you!
😊Master Python by YashTech Institute :- th-cam.com/video/RmOIz0UC654/w-d-xo.htmlsi=jcY1UHsWNmXHoIcH
Great video!
class me babita & jethalal he 😂😂
Thanks!! ❤
thanks
This helped me Thanks
www.praudyog.com/python/
hi sir plseas send python syllabus for machine learning
Reffer w3 schools .clearly ull get details
You are great sir. nice video
Isna Not ijna . Good explanation
Explain about non autocorrlatiop
Thanku sir kya app role of data science bta skte hai
Thanks for your efforts for creation of this video . I understood concept .....
Nice sir
what if we have age > 100 how do we handle that?
very helpful tutorial, big thanks from Australia!
Thanks!, very clear!!
Nicely explained 👌
gurukul as in nithyananda's gurukuls?
I searched a lot but only found it in your video. Nice explanation and great efforts! keep it up
nicely explained
Very nice sir🙏🙏🙏
Very nicely explained sir. It's really helpful thank you
If we had 3 large data frames we need to merge those 3 . like data is in lake's what is the solution for that can you explain
learnt something new...thanks
what is the function if we want to convert any form to decimal
Thankyou so much Gurukul wisdom :)
Thank you so much for that clear tutorial.
Bro can you tell me how many total no. Of topics/concepts in python
Great Video Good Explaining!
Ty! help me alot.
but where are Decorators, Generators, Context Manager, Closures, Enumerators, Meta-Programming, Module and Packages.
Thanks for suggestion . Surely we will add those topics.
@@GurukulWisdom These are my list for python ● Beginner - a) Conditionals b) Control Flow c) Python Inbuild Data Structures {Lists, Tuples, Dictionaries, Sets} d) Functional Programming, Scopes and higher Order Functions {zip, map, filter} e) File IO ● Intermediate - a) Object Oriented Programming b) inheritance c) Comprehensions d) Data Structures {Trees, Graphs, Hash-Maps etc} e) Dunder Methods { __del__, __repr__, __str__ etc} f) Exceptions g) Module /Package and Import statement h) Static Python Programming ● Advanced - a) Decorators b) Generators c) Enumerators f) Meta-Programming / Meta-Classes g) Context Managers h) Concurrency and Parallelism i) Cython : Python Superset to give Speed similar to C/C++
Bro enka untaya topics python lo evenaaa
getting error as "typeError: Panel() takes no arguments" everytime i try to initilize a panel. Please help
Can you share with me the code which you are trying?
aapka channel khoob aage jayega bhai ..subscribed and shared with friends
How to deal with multiple columns
Sir you are explanation very nice and good information,this video helpful for me Thank you so much
Sir, please come up with more videoes like this
Is it possible to use replace=false for only a specific column? So that if a user x makes a post 1, 2, 3 we can just keep one post per user. Thanks in advance and thanks for the helpful tutorials!
Hello, Thanks for watching my videos. To answer your question you can use the pandas drop_duplicate() method. Example: df = df.drop_duplicates(subset=''user', keep="first") Here we are dropping the duplicate rows based on the "user" column. If the same user has three posts 1, 2, 3 it will drop the last two same users rows and will keep the first one as you have mentioned keep="first".
funny names though
Thank you for the tutorial! :) This was really helpful
Odiya ki tame. Improve communication skills bro.
i am pretty sure it is not a language comprehension lesson or is it?
bhaluj
Many thanks for your video. it was very clear and well explained. I have one question how do I use the random method of pandas to randomly generate a new dataset form from old dataset
you are great in teaching
Good explanation bro. Print exception type as well so that we’ll know which exception is raised. All the best.
The names r inspired from taarak Mehta ka ooltah chashma 😂😂😂
# I have initialized four different arrays that is a, b, c and d # I want to update array a and array c # I want to update only those indexes of array a from array b, and # I want to update array c from array d # I again repeat only those indexes where the elements of array b is greater than the elements in array a # and the same corresponding indexes (based on the above condition) of array c will be updated from array d import numpy as np a = np.array([27, 10, 14, 15]) b = np.array([26, 40, 13, 75]) c = np.array([11, 22, 33, 44]) d = np.array([10, 20, 30, 40]) for i in range(len(a)): if b[i] > a[i]: a[i] = b[i] # stores the maximum of array a and array b in array a c[i] = d[i] # store the corresponding value of array d in array c print(a) print(c) # Is there any one line code for the entire loop statement
What is useful information. Many thanks for you great explanation.
Nice