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What you'll get

  • Job Credibility
  • Certification Valid for Life
  • On-demand video*
  • E-Book
  • Self-Paced Learning
  • Certificate of Completion

Exam details

  • 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.

Course Content

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


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