Mam.. I am having one doubt... Lets say user U1 and U2 is similar and there Pearson correlation is 1. But their scale is different... Example....... User U1 ratting => 1.5, 2.0, 2.5, 3.0, 3.5 , 3.6 User U2 ratting => 0.5, 1.5, 2.5, 3.5, 4.5, ?? Now for user U2, if I apply the p(u,i) .... Then for user U2 it will return 3.6 [2.5 + 1*(3.6-2.5)/1].... But it should be 4.7 Am I making any mistake?
in real world you wouldn't find any structure data...you have to clean that dump, then you can decide which approach you can go for..i am working for a comp..i follow svd approach..its all depend on the data set. believe me the most hectic thing is to clean that data set..
Ma'am you are amazing I don't know y this video is underrated people should watch this video
Machine Learning by Prof. Sudeshna Sarkar
Basics
1. Foundations of Machine Learning (ua-cam.com/video/BRMS3T11Cdw/v-deo.html)
2. Different Types of Learning (ua-cam.com/video/EWmCkVfPnJ8/v-deo.html)
3. Hypothesis Space and Inductive Bias (ua-cam.com/video/dYMCwxgl3vk/v-deo.html)
4. Evaluation and Cross-Validation (ua-cam.com/video/nYCAH8b5AQ0/v-deo.html)
5. Linear Regression (ua-cam.com/video/8PJ24SrQqy8/v-deo.html)
6. Introduction to Decision Trees (ua-cam.com/video/FuJVLsZYkuE/v-deo.html)
7. Learning Decision Trees (ua-cam.com/video/7SSAA1CE8Ng/v-deo.html)
8. Overfitting (ua-cam.com/video/y6SpA2Wuyt8/v-deo.html)
9. Python Exercise on Decision Tree and Linear Regression (ua-cam.com/video/lIBPIhB02_8/v-deo.html)
Recommendations and Similarity
10. k-Nearest Neighbours (ua-cam.com/video/PNglugooJUQ/v-deo.html)
11. Feature Selection (ua-cam.com/video/KTzXVnRlnw4/v-deo.html )
12. Feature Extraction (ua-cam.com/video/FwbXHY8KCUw/v-deo.html)
13. Collaborative Filtering (ua-cam.com/video/RVJV8VGa1ZY/v-deo.html)
14. Python Exercise on kNN and PCA (ua-cam.com/video/40B8D9OWUf0/v-deo.html)
Bayes
16. Baiyesian Learning (ua-cam.com/video/E3l26bTdtxI/v-deo.html)
17. Naive Bayes (ua-cam.com/video/5WCkrDI7VCs/v-deo.html)
18. Bayesian Network (ua-cam.com/video/480a_2jRdK0/v-deo.html)
19. Python Exercise on Naive Bayes (ua-cam.com/video/XkU09vE56Sg/v-deo.html)
Logistics Regession and SVM
20. Logistics Regression (ua-cam.com/video/CE03E80wbRE/v-deo.html)
21. Introduction to Support Vector Machine (ua-cam.com/video/gidJbK1gXmA/v-deo.html)
22. The Dual Formation (ua-cam.com/video/YOsrYl1JRrc/v-deo.html)
23. SVM Maximum Margin with Noise (ua-cam.com/video/WLhvjpoCPiY/v-deo.html)
24. Nonlinear SVM and Kernel Function (ua-cam.com/video/GcCG0PPV6cg/v-deo.html)
25. SVM Solution to the Dual Problem (ua-cam.com/video/Z0CtYBPR5sA/v-deo.html)
26. Python Exercise on SVM (ua-cam.com/video/w781X47Esj8/v-deo.html)
Neural Networks
27. Introduction to Neural Networks (ua-cam.com/video/zGQjh_JQZ7A/v-deo.html)
28. Multilayer Neural Network (ua-cam.com/video/hxpGzAb-pyc/v-deo.html)
29. Neural Network and Backpropagation Algorithm (ua-cam.com/video/T6WLIbOnkvQ/v-deo.html)
30. Deep Neural Network (ua-cam.com/video/pLPr4nJad4A/v-deo.html)
31. Python Exercise on Neural Networks (ua-cam.com/video/kTbY20xlrbA/v-deo.html)
Computational Learning Theory
32. Introduction to Computational Learning Theory (ua-cam.com/video/8hJ9V9-f2J8/v-deo.html)
33. Sample Complexity: Finite Hypothesis Space (ua-cam.com/video/nm4dYYP-SJs/v-deo.html)
34. VC Dimension (ua-cam.com/video/PVhhLKodQ7c/v-deo.html)
35. Introduction to Ensembles (ua-cam.com/video/nelJ3svz0_o/v-deo.html)
36. Bagging and Boosting (ua-cam.com/video/MRD67WgWonA/v-deo.html)
Clustering
37. Introduction to Clustering (ua-cam.com/video/CwjLMV52tzI/v-deo.html)
38. Kmeans Clustering (ua-cam.com/video/qg_M37WGKG8/v-deo.html)
39. Agglomerative Clustering (ua-cam.com/video/NCsHRMkDRE4/v-deo.html)
40. Python Exercise on means Clustering (ua-cam.com/video/qs7vES46Rq8/v-deo.html)
Tutorial I (ua-cam.com/video/uFydF-g-AJs/v-deo.html)
Tutorial II (ua-cam.com/video/M6HdKRu6Mrc/v-deo.html )
Tutorial III (ua-cam.com/video/Ui3h7xoE-AQ/v-deo.html)
Tutorial IV (ua-cam.com/video/3m7UJKxU-T8/v-deo.html)
Tutorial VI (ua-cam.com/video/b3Vm4zpGcJ4/v-deo.html)
Solution to Assignment 1 (ua-cam.com/video/qqlAeim0rKY/v-deo.html)
Madam lectures were very precious to me.l had followed her for the past 11 years through CD's.
Mam,
your video is really superb. Can you please show us, how to run this collaborative model on R?.
Respected Madam,
Very nice teaching.
Mam.. I am having one doubt...
Lets say user U1 and U2 is similar and there Pearson correlation is 1. But their scale is different...
Example.......
User U1 ratting => 1.5, 2.0, 2.5, 3.0, 3.5
, 3.6
User U2 ratting => 0.5, 1.5, 2.5, 3.5, 4.5, ??
Now for user U2, if I apply the p(u,i) .... Then for user U2 it will return 3.6 [2.5 + 1*(3.6-2.5)/1].... But it should be 4.7
Am I making any mistake?
Nice and easy explanation
thanks a lot maam.. It was a great intro.!
A real world example would help the viewer to understand better.
in real world you wouldn't find any structure data...you have to clean that dump, then you can decide which approach you can go for..i am working for a comp..i follow svd approach..its all depend on the data set. believe me the most hectic thing is to clean that data set..
@@Pattnaik03420 what methods do you use for cleaning the dataset in solving real problems in the company