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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Virtual Classroom	Video and Text Based

Course Overview

This “Artificial Intelligence + Deep Learning” certification is a short-term online course by E&ICT Academy, IIT Kanpur. This course is designed for working professionals who want to understand and learn what exactly is Artificial Intelligence, and its core concept like Deep Learning.

The “Artificial Intelligence + Deep Learning” syllabus comprises topics like Machine Learning, Deep Learning, TensorFlow, Artificial Neural Network, Neural Networks, and many more concepts.

The “Artificial Intelligence + Deep Learning” Training is a very economical course that can be done sitting at home or without even having to leave jobs. This kind, of course, is prepared for all kinds of graduates, or post-graduates. This course once completed, and certificate adding will add great value to a candidates’ hiring process by prospective employers.

The Highlights

An Online Course
Certification by E&ICT Academy, IIT Kanpur
6 weeks access to course after completion
Doubt clearing sessions
Hands-on practical exposure
Online MCQ exam

Programme Offerings

Online Classes
Online Live Training
Certification by E&ICT Academy
IIT Kanpur
MCQ exam

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IIT Kanpur

The candidates have to pay Rs. 20,000 and with it 18% extra GST charges for the “Artificial Intelligence + Deep Learning” Certification fee. Sometimes different coupon codes are also available for getting discounts.

Artificial Intelligence + Deep Learning Fee Structure

Description	Amount
Artificial Intelligence + Machine Learning	Rs. 20,000 + 18 % GST Extra

Eligibility Criteria

Educational Qualification

Candidates with any educational qualification under any discipline can opt for the course.

Work Experience

No work experience is required, but candidates with a knowledge of C/C++ can be an added advantage.

Certificate Qualifying Details

All candidates must attend the lectures, finish all the assignments, and attend the online exam for the course completion certificate.

What you will learn

Knowledge of Artificial IntelligenceKnowledge of deep learning

After course completion, candidates should be able to:

Candidates will have a proper understanding of the concepts of Artificial Intelligence
Candidates will be able to have access to hands-on-real world projects.

Who it is for

Anyone who wants to develop a career in Artificial Intelligence and Deep Learning can opt for this course. Also, this course is also designed for people who are working professionals and are either a Bachelor’s degree holder in Technology, computer application, and a master’s degree holder in computer application, and Technology.

Admission Details

Candidates may follow the steps below for admission to the “Artificial Intelligence + Deep Learning” course:

Step 1: Visit the course website: https://ict.iitk.ac.in/product/artificial-intelligence-deep-learning-with-project-letter/.

Step 2: Next Create an Account for the enrollment.

Step 3: Then login with the account credentials created.

Step 4: Then pay the fees required to get enrolled.

Step 5: Start studying after the admission is confirmed.

Application Details

Application form filling is not necessary for the “Artificial Intelligence + Deep Learning” certification. The candidates can create an account, log in, and pay the fees to get admission.

The Syllabus

Overview of Machine Learning
Difference between AI, ML, and DL
Applications of ML and DL
Types of Machine Learning
Linear Regression
Logistic Regression
Overfitting and underfitting
K-Nearest Neighbor
Cross-validation and Hyper-parameter tuning
Confusion Matrix, Recall, Precision
K-Means Clustering

What is the Artificial Neural Network (ANN)?
How Neural Network Works?
Perceptron
Multilayer Perceptron
Feed Forward
Gradient Descent and Stochastic Gradient Descent
Back propagation

What is Deep Learning?
Deep Learning Packages
Deep Learning Applications
Building Deep Learning Environment
Installing TensorFlow Locally
Working with Google Colab

What is TensorFlow?
TensorFlow 1.x V/S TensorFlow 2.x
Placeholder, Variables, Constants
Operations using TensorFlow
Difference between TensorFlow and NumPy operations
Computational Graph
Visualizing Graph using Tensorboard

What are Activation Functions?
Sigmoid Function,
Hyperbolic Tangent Function (tanh)
ReLU – Rectified Linear Unit
Softmax Function
Vanishing Gradient Problem

Understanding MNIST Dataset
Initializing weights and biases
Defining loss/cost Function
Train the Neural Network
Minimizing the loss by adjusting weights and biases

SGD with Momentum
RMSprop
AdaGrad
Adam
Dropout Layers and Regularization
Batch Normalization

What is Keras?
Keras Fundamental For Deep Learning
Keras Sequential Model and Functional API
Solve a Linear Regression and Classification Problem with Example
Saving and Loading a Keras Model

Introduction to CNN
CNN Architecture
Convolutional Operations
Pooling, Stride, and Padding Operations
Data Augmentation
Building, Training and Evaluating First CNN Model
Model Performance Optimization
Autoencoders for CNN
Transfer Learning

Introduction to RNN
RNN Architecture
Types of RNN
Implementing basic RNN in TensorFlow
Need for LSTM and GRU
Deep RNN
Text Classification Using LSTM

Sentiment analysis using RNN
MNIST Handwritten digits classification using CNN
Cat vs Dog Image classification
Objects Detection from Yolov

Evaluation process

After every course, there is an assessment. And finally, after the end of all the courses, and assessments, a final online MCQ based examination is held that students have to pass with at least 60% marks to get the certificate of completion.