26:14

Leetcode 151 (2): Reverse Words in a List | Approach 3: Inplace Modification

23:58

Leetcode 151: Reverse Words in a String | 3 Approaches Including Follow up for Inplace Modification

25:41

Leetcode Problem 98 (2): Validate BST | Role of Return Statement and Conditional Recursion

44:00

Leetcode problem 98: Validate Binary Search Tree in Detail

13:28

Lecture 1: 3D Machine Learning - Introduction to Voxel (3D Grid)

15:08

ML Lecture 1.10 : Unigrams, Bigrams on IMDB dataset

09:26

ML 1.9 : Bag of Words Approach

21:33

ML 1.8 : Case Study with Python Code - Naive Bayes for IMDB Movie Review Classification

14:17

ML 1.7: Naive Bayes with Examples

16:18

Lecture 5.1 : Sigmoid Function in 3D with Python Code

25:39

Lecture 5: Sigmoid Neuron/ Need for Smoother Decision Boundary

17:28

Lecture 4: DL- Perceptron Drawbacks/ Need for Non-linear Activation Functions

16:12

Lecture 1.1 : MP Neuron Python Hands-on, Loading and Visualizing the dataset | Train-test Split

13:08

Lecture 1: Deep Generative Models | Introduction to Auto-encoders | Variational Auto-encoders

13:25

Lecture 8: Solved Problems on Conditional Probability

13:38

Lecture 7: An intuitive introduction to Conditional Probability

17:30

Lecture 6: Permutations and Combinations

11:55

Lecture 5: Basic Principle of Counting | Permutations | Intuitive Explanation

11:20

Lecture 4: Frequency based Probability definition

09:02

Lecture 3: Axioms of Probability

09:52

Lecture 2 : Basics of Probability

08:38

Introduction to Statistics (for Data Science)

09:41

Lecture 3: Deep Learning with Python Hands-on: Perceptron Learning Algorithm Convergence

14:00

Lecture 2 : Deep Learning with Python Hands-on: Perceptron Learning Algorithm

16:33

Difference Between Scalar/Vector/Matrix/ and Tensors with Python Hands-on | Part 2

27:22

But What is a Tensor? With Python hands on with TensorFlow | Part 1

11:54

Machine Learning Lecture 8.2 : Bias-Variance Trade-off in detail

19:24

Machine Learning Lecture 8.1 : Ensemble Learning with python Code

08:03

Machine Learning Lecture 4.4 : Overfitting and Underfitting in Decision Trees

10:44

Machine Learning Lecture 4.5: Decision Tree Optimization by Pre-Pruning and Post-Pruning | with Code

20:49

Machine Learning Lecture 4.3 : Decision Tree Classifier based on Gini Impurity with Python Code

11:54

Python Practice : How to Print various patterns in Python

29:59

Machine Learning Lecture 4.2 : Building a Decision Tree based on Information Gain/Entropy measure

17:58

ML 1.6 : Naive Bayes Algorithm

06:59

Machine Learning Lecture 4.1 : Introduction to Decision Tree

14:41

Machine Learning Lecture 3.2 : Soft Margin SVM Mathematical Derivation

31:36

Machine Learning Lecture 3.1 : Support Vector Machine | Introduction to Hard and Soft Margin SVM

31:27

Machine Learning Lecture 2.1 : Linear Regression Theory and Hands-on with Python

23:42

Lecture 1 - Deep Learning with Python Hands-on : Mcculloch-Pitts Neuron

24:28

Attention is all you need (NLP Transformer Model) Research paper by Google Brain | Deep Learning

16:02

Solved Example on ANOVA test | Step by Step Calculation

16:12

ANOVA Test : Derivation of Test Statistic Part 2

24:59

ANOVA Test: Explanation with Derivation of Test Statistic

10:12

ANOVA test | Chi - Square Random variable | Introduction to Analysis of Variance

25:18

Lecture 2: Proof of z-test | Hypothesis Testing | Best ever Explanation

13:51

Lecture 1 : Introduction to Hypothesis Testing

24:27

Python Code for Gaussian Filtering (Horizontal and Vertical Edge Detection)

38:59

Lecture 3 : Image Processing and Computer Vision : Canny Edge Detection

35:47

Lecture 2 : Image Processing and Computer Vision : Non-Linear Filtering and Image/Gaussian Pyramids

38:24

Lecture 1 : Image Processing and Computer Vision : Image Filtering

11:12

Python np.meshgrid( ) Function

19:21

Phase Shift Keying

31:39

Pulse Amplitude Modulation (Amplitude Shift Keying)

17:34

Average Self Information : Entropy

26:22

Information Theory : Mutual Information and Self Information

15:24

Consistency of Spectral Estimators(DIgital Signal Processing)

16:44

Non-Parametric Spectrum Estimation Methods: Periodogram

06:39

Introduction to Power Spectrum Estimation(Digital Signal Processing)

14:58

Complex Baseband Representation of a Passband Signal

12:24

Digital Communication Systems : Modulator Block Diagram