MODULE 01 : OVERVIEW OF AI

MODULE 02 : Introduction of Machine Learning and Deep Learning

MODULE 03 : Model Training and Testing 

MODULE 04 : Overfitting and Underfitting

MODULE 05 : Types of Supervised Learning Algorithm

MODULE 06 : Types of Unsupervised Learning Algorithms

MODULE 07 : Module Confusion Matrix

MODULE 08 : Simple linear Regression(introduction )

MODULE 09 : Implementation of Simple linear Regressions

MODULE 10 : Theory of Multiple linear regression

MODULE 11 : Implementation of polynomial regression in python

MODULE 12 : Theory of polynomial regression

MODULE 13 : Implementation of polynomial regression in python

MODULE 14 : Theory of logistics regression

MODULE 15 : Practically implement logistics regression in python

MODULE 16 : Theoretically Part of KNN(K-Nearest Neighbors)

MODULE 17 : Theoretically part of SVM(Support Vector Machine)

MODULE 18 : Practically Implementation of SVM(support vectors machine ) in Python

MODULE 19 : Theoretically Part of Naive Bayes Algo

MODULE 20 : Practically Implementation of Naive Bayesh Algo using Pyhton

MODULE 21 : Theoretically Part of Decision Tree Classifier

MODULE 22 : Practically Part Of Decision Tree in Python Programming

MODULE 23 : Theoretically Part of Random Forest Algorithm

MODULE 24 : Practicality Implementation of Random Forest Algo

MODULE 25 : Concepts of Overfitting and Underfitting

MODULE 26 : Introduction of Unsupervaised Machine Learning

MODULE 27 : Introduction of Unsupervaised Machine Learning

MODULE 28 : Introduction of k-means Clustering

MODULE 29 : Practically Implementation of K-means Clustering Using Python

MODULE 30 : Introduction of Deep learning

MODULE 31 : About Neural Network

MODULE 32 : Types of Neural Network

MODULE 33 : Introduction of CNN

MODULE 34 : About 1D and 2 Data

MODULE 35 : Image Processing and Computer Vision

MODULE 36 : About Max-Pooling

MODULE 37 : What is Flattening?

MODULE 38 : What is Activation Functions?

MODULE 39 : Types of Activation Function

MODULE 40 : Linear Activation Function

MODULE 41 : Threshold Activation Function

MODULE 42 : Sigmoid Activation Function

MODULE 43: Relu Function

MODULE 44 : Leaky Relu Function

MODULE 45 : SoftMax Activation

MODULE 46 : Back Propogation

MODULE 47 : Loss Function

MODULE 48 : About Colab

MODULE 49 : What is bias?

MODULE 50 : Gradient Dissent

MODULE 51 : About Libeary

MODULE 52 : About Ann

MODULE 53 : How to Download Dataset

MODULE 54 : Fetch Dataset in a Colab

MODULE 55 : Divide the Data into two Part

MODULE 56 : Handle Cotogoriacal Data

MODULE 57 : Split Data Into Training and Testing

MODULE 58 : Reshape the Dataset

MODULE 59 : Importing Libeary

MODULE 60 : Adding One Input Layr

MODULE 61 : Adding Second Input Layer

MODULE 62 : About CNN

MODULE 63 : How to Load Data in Drive

MODULE 64 : How to Load Data Into a Colab from Drive

MODULE 65 : Importind Libeary for CNN

MODULE 66 : Step to Build CNN Model

MODULE 67 : Inital the CNN and Make Convolutional

MODULE 68 : Repaeat the 1 and 2 and Make Flatten Layer

MODULE 69 : Full Connection Buid in CNN

MODULE 70 : Compile the CNN

MODULE 71 : Train the CNN Algo

MODULE 72 : Test the CNN Algo

Introduction of Deep learning