Have any question ?
+91 8106-920-029
+91 6301-939-583
team@appliedaicourse.com
Register
Login
COURSES
Applied Machine Learning Course
Diploma in AI and ML
GATE CS Blended Course
Interview Preparation Course
AI Workshop
AI Case Studies
Courses
Applied Machine Learning Course
Workshop
Case Studies
Job Guarantee
Job Guarantee Terms & Conditions
Incubation Center
Student Blogs
Live Sessions
Success Stories
For Business
Upskill
Hire From Us
Contact Us
Home
Courses
Applied Machine Learning Online Course
nu-SVM: control errors and support vectors
nu-SVM: control errors and support vectors
Instructor:
Applied AI Course
Duration:
6 mins
Full Screen
Close
This content is restricted. Please
Login
Prev
Next
Train and run time complexities
SVM Regression
Support Vector Machines (SVM)
1.1
Geometric Intution
20 min
1.2
Mathematical derivation
32 min
1.3
Why we take values +1 and and -1 for Support vector planes
9 min
1.4
Loss function (Hinge Loss) based interpretation
18 min
1.5
Dual form of SVM formulation
16 min
1.6
kernel trick
10 min
1.7
Polynomial Kernel
11 min
1.8
RBF-Kernel
22 min
1.9
Domain specific Kernels
6 min
1.10
Train and run time complexities
8 min
1.11
nu-SVM: control errors and support vectors
6 min
1.12
SVM Regression
8 min
1.13
Cases
9 min
1.14
Code Sample
14 min
1.15
Revision Questions
30 min
Interview Questions on Support Vector Machine
2.1
Questions & Answers
30 min
Decision Trees
3.1
Geometric Intuition of decision tree: Axis parallel hyperplanes
17 min
3.2
Sample Decision tree
8 min
3.3
Building a decision Tree:Entropy
19 min
3.4
KL Divergence
14 min
3.5
Building a decision Tree:Information Gain
10 min
3.6
Building a decision Tree: Gini Impurity
7 min
3.7
Building a decision Tree: Constructing a DT
21 min
3.8
Building a decision Tree: Splitting numerical features
8 min
3.9
Feature standardization
4 min
3.10
Building a decision Tree:Categorical features with many possible values
7 min
3.11
Overfitting and Underfitting
8 min
3.12
Train and Run time complexity
7 min
3.13
Regression using Decision Trees
9 min
3.14
Cases
12 min
3.15
Code Samples
9 min
3.16
Revision Questions
30 min
Interview Questions on decision Trees
4.1
Questions & Answers
30 min
Ensemble Models
5.1
What are ensembles?
6 min
5.2
Bootstrapped Aggregation (Bagging) Intuition
17 min
5.3
Random Forest and their construction
15 min
5.4
Bias-Variance tradeoff
7 min
5.5
Bagging Train and run time complexity
9 min
5.6
Bagging:Code Sample
4 min
5.7
Extremely randomized trees
8 min
5.8
Random Forest :Cases
6 min
5.9
Boosting Intuition
17 min
5.10
Residuals, Loss functions and gradients
13 min
5.11
Gradient Boosting
10 min
5.12
Regularization by Shrinkage
8 min
5.13
Train and Run time complexity
6 min
5.14
XGBoost: Boosting + Randomization
14 min
5.15
AdaBoost: geometric intuition
7 min
5.16
Stacking models
22 min
5.17
Cascading classifiers
15 min
5.18
Kaggle competitions vs Real world
9 min
5.19
Revision Questions
30 min
Module 4: Live Sessions
6.1
Code Walkthrough: Models in Scikit-Learn
6.2
Interview Questions on SVMs
6.3
Interview Questions on Boosting and Bagging
6.4
Attribution Models in Marketing
6.5
Attribution Models in Marketing-Part 2
6.6
LIVE_ Monte Carlo Simulations
6.7
Which ML/DL technique to use where?
Close