Online Artificial Intelligence Course In Delhi ,Training & Certification In Delhi

Online Artificial Intelligence Course in Delhi

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Online Artificial Intelligence Course in Delhi

It Includes the power of Data Science, Machine Learning to create powerful Artificial Intelligence applications with the help of Artificial Intelligence training in Delhi.During the course, the students will also have the opportunity to participate in the organization, competitions, events, live projects of the largest industrial research organization in the world, and network with other top students in different fields from other universities. Artificial Intelligence Course works like a computer program that is designed to do smart work. Craw Security Delhi offers the best Artificial Intelligence course preparation in Delhi with an Artificial Intelligence course schedule planned as per the business requests that assist the students with getting guaranteed arrangements and procuring their fantasy occupations at different MNCs.

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Artificial Intelligence course at Craw Security

CRAW Security invites applicants or candidates for an Artificial Intelligence Course in Delhi, Python learning, machine learning Program in New Delhi. We are seeking highly motivated students, who are interested in experiencing the excitement of research. The selected students will have the chance to work intimately with a remarkable research team on challenging problems that range from leading-edge exploratory work to prototyping real-world systems and applications.

Artificial Intelligence Course Content (Hindi)

Course Curriculum

MODULE 01 : OVERVIEW OF AI

Introduction 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

Test the CNN Algo

03:48
73 – Intro of rnn

04:18
74 – About lstm

03:17
75 – Module about dataset

02:15
76 – Fetch dataset from local into the colab

06:08
77 – About libeary

01:41
78 – Importing libeary

07:10
79 – Read data set using pandas

02:55
80 – Show data in a countplot using seaborn

02:57
81 – Divide data into two part x and y

00:09
82 – Apply tokenizer

04:08
83 – Build rnn model

06:26
84 – Compile rnn

04:07
85 – Fit the rnn

04:30
86 – Test the model

03:40
87 – Show accurecy

04:33
88 – About open cv

02:44
89 – Instalation of opencv

02:14
90 – Read and show images

05:49
91 – Show image properties using open c

02:55

Artificial Intelligence Our Course Advisor

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Course Highlights

Understand the theory behind Artificial Intelligence
Solve Real World Problems with AI
Master the State of the Art AI model
Research Areas of Artificial Intelligence
Intelligence System
Robotics
Artificial Intelligence Issues

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Classroom Training

We offer customized VILT (Virtual Instructor-Led Training) sessions at your convenient hours to provide effortless training.

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Online Training Class

One can also opt for the prerecorded video sessions available at any point of time from any particular
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Corporate Training

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Online Artificial Intelligence Course in Delhi

Book a Trial Demo Class

Online Artificial Intelligence Course in Delhi

Artificial Intelligence course at Craw Security

Artificial Intelligence Course Content (Hindi)

Course Curriculum

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 Network

MODULE 32 : Types of Neural Network

Types of Neural Network

MODULE 33 : Introduction of CNN

Introduction of CNN

MODULE 34 : About 1D and 2 Data

About 1D and 2 Data

MODULE 35 : Image Processing and Computer Vision

Image Processing and Computer Vision

MODULE 36 : About Max-Pooling

About Max-Pooling

MODULE 37 : What is Flattening?

What is Flattening?