31:22

Complete CNN Project using Mnist Dataset (70 k images)

26:20

Understanding Convolutional Neural Networks (CNN)

29:54

MPG Car regression complete project using DNN

19:06

Simple Regression Project using DNN

39:01

Batches, Epochs, Gradient problems, Optimizers

38:42

Introduction to Deep Learning , Batch normalization Learning rate Dropout Augmentation

14:05

Binary Breast cancer classification Project using ANN

42:56

Multiclassification Project using DNN for Mnist dataset

21:24

Role of Activation function in backpropagation

31:09

Back propagation theory for MLP and Numerical Example

52:49

Fead forward Neural Network (FFNN) and Single layer perceptron (SLP)

17:30

Activations functions for ANN

46:19

What is Neural Networks

44:27

Fake new Classification Complete Project using NLP

31:55

NLP POS , Syntactic Parsing and TFIDF

20:19

Bag of words and N grams technique

30:24

NLP and Preprocessing steps (Tokenization, Lemmatization, stemming, ..)

28:47

#51: Cancer types classification

08:38

#52: Glioma Grading classification

18:04

#50: Intrusion detection full project

39:16

#49: Complete Machine learning Project with many Algorithms Student Attrition dataset

55:37

Module 3: Network Security Concepts

24:45

#48: Dimension reduction techniques (PCA, LDA, TSNE)

29:42

Module 15: Floating and Summary static routing

24:57

#47: Outlier detection techniques (IQR, Z-Score, Isolation Forest, LOF , Elliptic Envelope)

18:44

#46: Feature selection (RFE, RFECF, BFS, FFS, select from model) Part 2

51:42

#45: Feature selection (Kbest, select percentile, ch2) with complete project

39:16

#44: Complete ML Algorithms with Student Attrition dataset

01:14:42

#43: Ensemble ML Algorithms (Voting, Bagging, Boosting, Stacking )

29:44

#42: Clustering Customers Income using (DBSCAN, Hierarchical, K-means )

28:26

#41: K-means complete project

07:38

Random forest Algorithm understanding

14:13

Module 5 : Numbering systems

01:02:12

#28 : Support Vector Machine and it's parameters

24:27

#27 : Classification metrics (Accuracy, precision, recall, ROC AUC, Macro, Micro, and weighted)

29:09

Module 13 : Network Virtualization

51:45

Module 12 : Network Troubleshooting

30:16

Module 11 : Network Design

09:37

#40 : silhouette Score for calculating Clustering performance

31:54

# 39 : DBSCAN clustering Algorithm Explanation

19:18

#38 : Hierarchical clustering AGNES and DIANA

32:54

#37 : Clustering concepts and Kmeans Algorithm with Example

28:44

#36 : Complete Project using RF and DT with EDA

35:17

Module 2 : Practical OSPF Accumulation Cost Part 3

35:04

Module 1 : Single Area OSPF concepts

44:49

#35 : Complete KNN Project with EDA

33:49

#34 : Naive bayes Complete Project Tutorial & EDA Gaussian, Multinomial, Bernoulli NB

37:20

#33 : Car evaluation classification using Decision Tree Project and EDA

29:33

#32 : KNN and Naive Bayes Algorithms

31:09

#31 : Decision Tree Algorithm

24:42

#30 : Breast Cancer classification with correlation feature selection and EDA

52:43

#29 : SVM Complete project tutorial with EDA

36:11

#26 : EDA with Complete project Part 4

38:05

#25 : EDA with Complete project Part 3

43:19

#24 : EDA with Complete project Part 2

49:20

# 23 : EDA (Exploratory Data Analysis) with Complete Project Part 1

35:26

#22 : Logistic regression Mathematics

38:10

#21 : Practical examples on linear regression

43:39

#20 : Linear regression with multiple variables & scaling techniques & overfitting and underfiting

16:20

#19: Linear regression with one variable Part 2