MODULE 01 : OVERVIEW OF AI
MODULE 02 : Introduction of Machine Learning and Deep Learning
MODULE 03 : Model Training and Testing
Model Training and Testing
MODULE 04 : Overfitting and Underfitting
Overfitting and Underfitting
MODULE 05 : Types of Supervised Learning Algorithm
Types of Supervised Learning Algorithm
MODULE 06 : Types of Unsupervised Learning Algorithms
Types of Unsupervised Learning Algorithms
MODULE 07 : Module Confusion Matrix
Module Confusion Matrix
MODULE 08 : Simple linear Regression(introduction )
Simple linear Regression(introduction )
MODULE 09 : Implementation of Simple linear Regressions
Implementation of Simple linear Regressions
MODULE 10 : Theory of Multiple linear regression
Theory of Multiple linear regression
MODULE 11 : Implementation of polynomial regression in python
Implementation of polynomial regression in python
MODULE 12 : Theory of polynomial regression
Theory of polynomial regression
MODULE 13 : Implementation of polynomial regression in python
Implementation of polynomial regression in python
MODULE 14 : Theory of logistics regression
Theory of logistics regression
MODULE 15 : Practically implement logistics regression in python
Practically implement logistics regression in python
MODULE 16 : Theoretically Part of KNN(K-Nearest Neighbors)
Theoretically Part of KNN(K-Nearest Neighbors)
MODULE 17 : Theoretically part of SVM(Support Vector Machine)
Theoretically part of SVM(Support Vector Machine)
MODULE 18 : Practically Implementation of SVM(support vectors machine ) in Python
Practically Implementation of SVM(support vectors machine ) in Python
MODULE 19 : Theoretically Part of Naive Bayes Algo
Theoretically Part of Naive Bayes Algo
MODULE 20 : Practically Implementation of Naive Bayesh Algo using Pyhton
Practically Implementation of Naive Bayesh Algo using Pyhton
MODULE 21 : Theoretically Part of Decision Tree Classifier
Theoretically Part of Decision Tree Classifier
MODULE 22 : Practically Part Of Decision Tree in Python Programming
Practically Part Of Decision Tree in Python Programming
MODULE 23 : Theoretically Part of Random Forest Algorithm
Theoretically Part of Random Forest Algorithm
MODULE 24 : Practicality Implementation of Random Forest Algo
Practicality Implementation of Random Forest Algo
MODULE 25 : Concepts of Overfitting and Underfitting
Concepts of Overfitting and Underfitting
MODULE 26 : Introduction of Unsupervaised Machine Learning
Introduction of Unsupervaised Machine Learning
MODULE 27 : Introduction of Unsupervaised Machine Learning
Introduction of Unsupervaised Machine Learning
MODULE 28 : Introduction of k-means Clustering
Introduction of k-means Clustering
MODULE 29 : Practically Implementation of K-means Clustering Using Python
Practically Implementation of K-means Clustering Using Python
MODULE 30 : Introduction of Deep learning
MODULE 31 : About Neural Network
About Neural Network3:55
MODULE 32 : Types of Neural Network
Types of Neural Network2:27
MODULE 33 : Introduction of CNN
Introduction of CNN4:07
MODULE 34 : About 1D and 2 Data
About 1D and 2 Data2:26
MODULE 35 : Image Processing and Computer Vision
Image Processing and Computer Vision3:17
MODULE 36 : About Max-Pooling
About Max-Pooling4:05
MODULE 37 : What is Flattening?
What is Flattening?4:12
MODULE 38 : What is Activation Functions?
What is Activation Functions?3:32
MODULE 39 : Types of Activation Function
Types of Activation Function2:28
MODULE 40 : Linear Activation Function
Linear Activation Function2:28
MODULE 41 : Threshold Activation Function
Threshold Activation Function1:41
MODULE 42 : Sigmoid Activation Function
Sigmoid Activation Function2:26
MODULE 43: Relu Function
Relu Function2:21
MODULE 44 : Leaky Relu Function
Leaky Relu Function2:34
MODULE 45 : SoftMax Activation
SoftMax Activation3:22
MODULE 46 : Back Propogation
Back Propogation6:43
MODULE 47 : Loss Function
Loss Function4:25
MODULE 48 : About Colab
About Colab4:11
MODULE 49 : What is bias?
What is bias?3:13
MODULE 50 : Gradient Dissent
Gradient Dissent3:45
MODULE 51 : About Libeary
About Libeary3:41
MODULE 52 : About Ann
About Ann5:30
MODULE 53 : How to Download Dataset
How to Download Dataset?5:08
MODULE 54 : Fetch Dataset in a Colab
Fetch Dataset in a Colab17:15
MODULE 55 : Divide the Data into two Part
Divide the Data into Two Part
MODULE 56 : Handle Cotogoriacal Data
Handle Cotogoriacal Data4:37
MODULE 57 : Split Data Into Training and Testing
Split Data Into Training and Testing11:16
MODULE 58 : Reshape the Dataset
Reshape the Dataset8:18
MODULE 59 : Importing Libeary
Importing Libeary00:00
MODULE 60 : Adding One Input Layr
Adding One Input Layr7:28
MODULE 61 : Adding Second Input Layer
Adding Second Input Layer5:29
MODULE 62 : About CNN
About CNN4:21
MODULE 63 : How to Load Data in Drive
How to Load Data in Drive4:00
MODULE 64 : How to Load Data Into a Colab from Drive
How to Load Data Into a Colab from Drive5:54
MODULE 65 : Importind Libeary for CNN
Importind Libeary for CNN10:47
MODULE 66 : Step to Build CNN Model
Step to Build CNN Model4:47
MODULE 67 : Inital the CNN and Make Convolutional
Inital the CNN and Make Convolutional4:34
MODULE 68 : Repaeat the 1 and 2 and Make Flatten Layer
Repaeat the 1 and 2 and Make Flatten Layer4:43
MODULE 69 : Full Connection Buid in CNN
Full Connection Buid in CNN3:21
MODULE 70 : Compile the CNN
Compile the CNN
MODULE 71 : Train the CNN Algo
Train the CNN Algo3:36
MODULE 72 : Test the CNN Algo
Test the CNN Algo3:48
73 – Intro of rnn4:18
74 – About lstm3:17
75 – Module about dataset2:15
76 – Fetch dataset from local into the colab6:08
77 – About libeary1:41
78 – Importing libeary7:10
79 – Read data set using pandas2:55
80 – Show data in a countplot using seaborn2:57
81 – Divide data into two part x and y0:09
82 – Apply tokenizer4:08
83 – Build rnn model6:26
84 – Compile rnn4:07
85 – Fit the rnn4:30
86 – Test the model3:40
87 – Show accurecy4:33
88 – About open cv2:44
89 – Instalation of opencv2:14
90 – Read and show images5:49
91 – Show image properties using open c2:55
Introduction of Deep learning
