SVD computation with example
Mayank Khanna
SVD computation with example
27:40
SVD basics
Mayank Khanna
SVD basics
26:42
Basis Transformation example
Mayank Khanna
Basis Transformation example
7:04
Basis Transformation
Mayank Khanna
Basis Transformation
12:31
Matrices
Mayank Khanna
Matrices
7:04
Statistics and Probability 101
Mayank Khanna
Statistics and Probability 101
26:36
T Distributed - Stochastic Neighborhood Embedding
Mayank Khanna
T Distributed - Stochastic Neighborhood Embedding
9:01
Conditional Probability and Applications in Analytics
Mayank Khanna
Conditional Probability and Applications in Analytics
12:29
Bayes Theorem and the Monty Hall Problem
Mayank Khanna
Bayes Theorem and the Monty Hall Problem
12:18
Naive Bayes Mathematical derivation
Mayank Khanna
Naive Bayes Mathematical derivation
11:39
Additive Smoothing
Mayank Khanna
Additive Smoothing
9:47
Log Probabilities
Mayank Khanna
Log Probabilities
6:02
Feature Importance
Mayank Khanna
Feature Importance
2:54
Imbalanced Data
Mayank Khanna
Imbalanced Data
9:03
Gaussain Naive Bayes
Mayank Khanna
Gaussain Naive Bayes
7:15
Probability Mass function and probability density function
Mayank Khanna
Probability Mass function and probability density function
18:29
Normal Distribution and the standard normal variate
Mayank Khanna
Normal Distribution and the standard normal variate
13:25
Sampling and Central Limit Theorem
Mayank Khanna
Sampling and Central Limit Theorem
14:24
Maximum Likelihood Estimation
Mayank Khanna
Maximum Likelihood Estimation
30:47
Confidence Intervals
Mayank Khanna
Confidence Intervals
14:58
Hypothesis Tests
Mayank Khanna
Hypothesis Tests
29:48
K Nearest Algorithm
Mayank Khanna
K Nearest Algorithm
6:04
Failure Cases of K NN
Mayank Khanna
Failure Cases of K NN
3:06
Limitations of K NN
Mayank Khanna
Limitations of K NN
7:00
Cross Validation and K fold cross validation
Mayank Khanna
Cross Validation and K fold cross validation
17:57
Accuracy
Mayank Khanna
Accuracy
6:10
Confusion Matrix
Mayank Khanna
Confusion Matrix
9:30
Precision, Recall and F1 Score
Mayank Khanna
Precision, Recall and F1 Score
4:46
Receiver Operating Characteristic Curve ROC
Mayank Khanna
Receiver Operating Characteristic Curve ROC
9:36
Log Loss
Mayank Khanna
Log Loss
7:45
R squared
Mayank Khanna
R squared
7:02
Simple Linear Regression
Mayank Khanna
Simple Linear Regression
29:24
Model Validation - Coefficient of determination
Mayank Khanna
Model Validation - Coefficient of determination
9:28
Model Validation - Hypothesis Testing
Mayank Khanna
Model Validation - Hypothesis Testing
9:37
QQ Plot
Mayank Khanna
QQ Plot
5:53
Stopwords, Stemming, Lemmitization
Mayank Khanna
Stopwords, Stemming, Lemmitization
17:23
Bag Of Words
Mayank Khanna
Bag Of Words
9:59
TF IDF
Mayank Khanna
TF IDF
11:14
W2V, Weighted W2V, TF IDF weighted W2V
Mayank Khanna
W2V, Weighted W2V, TF IDF weighted W2V
7:01
Decision Tree Intorduction
Mayank Khanna
Decision Tree Intorduction
4:38
Entropy
Mayank Khanna
Entropy
8:32
Information Gain
Mayank Khanna
Information Gain
9:37
Gini Impurity
Mayank Khanna
Gini Impurity
3:46
Overfitting and Underfitiing
Mayank Khanna
Overfitting and Underfitiing
2:53
Ensemble Models
Mayank Khanna
Ensemble Models
5:05
Bootstrap Aggregating (Bagging)
Mayank Khanna
Bootstrap Aggregating (Bagging)
9:41
Random Forest
Mayank Khanna
Random Forest
10:42
Extremely Randomized Trees
Mayank Khanna
Extremely Randomized Trees
3:33
Deep Learning Introduction
Mayank Khanna
Deep Learning Introduction
5:03
Perceptron
Mayank Khanna
Perceptron
8:20
Multi Layered Perceptron
Mayank Khanna
Multi Layered Perceptron
5:21
Training a single neuron model
Mayank Khanna
Training a single neuron model
12:04
Training a Deep Neural Network
Mayank Khanna
Training a Deep Neural Network
22:40
Vanishing and Exploding gradients
Mayank Khanna
Vanishing and Exploding gradients
7:58
Dropout
Mayank Khanna
Dropout
7:21
5 Pricing a call option using Binomial model
Mayank Khanna
5 Pricing a call option using Binomial model
25:26
4. Measurable Functions
Mayank Khanna
4. Measurable Functions
21:52
3. Partitions and Filterations
Mayank Khanna
3. Partitions and Filterations
7:54
2. Sigma Fields
Mayank Khanna
2. Sigma Fields
20:49
1. Fair Games
Mayank Khanna
1. Fair Games
14:51
Probability Theory in Finance - Series Introduction
Mayank Khanna
Probability Theory in Finance - Series Introduction
11:30
Principal Component Analysis - Theory and Derivation
Mayank Khanna
Principal Component Analysis - Theory and Derivation
20:18