Job Credibility
Certification Valid for Life
Live Classes
Certificate of Completion

Mode of Exam : Online
Duration : 1 Hour
Multiple Choice Questions are asked
No. of Questions are asked : 50
Passing Marks : 25 (50%)
There is no negative marking

Artificial intelligence is revolutionizing the world. AI Advancement creating a complex data structure that is harder to understand. Deep-learning helps in solving this problem. This course will help you to design and building artificial strategies. Deep learning is one of the most demanding tech skills, in this course we will help you to master this skill.

In this certification course, you will learn the basics of advanced deep learning. Such as fundamentals of deep learning and Keras, machine learning vs deep learning, understanding of Neural Networks, and more.

After completion of this specialization, you will get and certificate in Deep Learning. Which you can use in building your portfolio or next advanced learning.

We will teach you each key points which are relevant to boost up your career in AI. You will not have only theoretical knowledge but also will be knowing the implantation of deep learning in real industries.

The benefits of pursuing this course are, the market share of deep learning is about $18 billion, and growing at a rate of 42% until 2023. There many sectors where professionals with deep learning skills are needed. For example, healthcare, IT, e-commerce, and more.

There is no doubt that the demand for these professionals is high, this is the best-suited course for beginner to advance level. This course is advised for all interested students, Data Scientists, Software Engineers, and Data Analysts.

The best thing about this specialization is a beginner-level student can also understand it. Or if you have a basic understanding of statistics and mathematics then that will be better.

Total: 61 lectures

Diving into the ML ecosystem
Training ML algorithms from data
Introducing deep learning
Why is deep learning important today?

Introduction to Colaboratory
Introduction and setup of TensorFlow
Introduction and setup of Keras
Introduction to PyTorch
Introduction to Dopamine
Other deep learning libraries

Binary data and binary classification
Categorical data and multiple classes
Real-valued data and univariate regression
Altering the distribution of data
Data augmentation
Data dimensionality reduction
Ethical implications of manipulating data

Learning for a purpose
Measuring success and error
Identifying overfitting and generalization
The art behind learning
Ethical implications of training deep learning algorithms

The perceptron model
The perceptron learning algorithm
A perceptron over non-linearly separable data

The MLP model
Minimizing the error
Finding the best hyperparameters

Introduction to unsupervised learning
Encoding and decoding layers
Applications in dimensionality reduction and visualization
Ethical implications of unsupervised learning

Introducing deep belief networks
Making deep autoencoders
Exploring latent spaces with deep autoencoders

Introducing deep generative models
Examining the VAE model
Comparing a deep and shallow VAE on MNIST
Thinking about the ethical implications of generative models

Introduction to RBMs
Learning data representations with RBMs
Comparing RBMs and AEs

Wide neural networks
Dense deep neural networks
Sparse deep neural networks
Hyperparameter optimization

Introduction to convolutional neural networks
Convolution in n-dimensions
Convolutional layers
Pooling strategies
Convolutional neural network for CIFAR-10

Introduction to recurrent neural networks
Long short-term memory models
Sequence-to-vector models
Vector-to-sequence models
Sequence-to-sequence models
Ethical implications

Introducing adversarial learning
Training a GAN
Comparing GANs and VAEs
Thinking about the ethical implications of GANs

₹3,500 ₹9,500

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Certificate in Deep Learning