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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

The ‘Probabilistic Deep Learning with TensorFlow 2’ online course is a study about the building of probabilistic models with TensorFlow and the incorporation of probabilistic distributions into deep learning models such as Bayesian neural networks, normalizing flows, and variational autoencoders. This certification course is provided by the Coursera online education platform and the course modules are developed by the Imperial College London.

This program is an overview of the TensorFlow Probabilistic Library and is scheduled to be completed in fifty-two hours. The classes are guided by Dr. Kevin Webster(Senior Teaching fellow in Statistics) from the faculty of natural sciences at the department of mathematics in Imperial College London.

The ‘Probabilistic Deep Learning with TensorFlow 2’ enables students to receive a course completion certificate and the opportunity to learn the course at the preferred pace. The students are engaged with learning methodologies such as video lectures, graded assignments, and quizzes for evaluation.

The Highlights

Online mode
Fifty-two hours of content
Advanced level program
Audit mode
EMI options
Financial Aid
Flexible deadlines
Shareable certificate
Subtitles in Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

Programme Offerings

videos
Reading
Practice Exercises
Course Modules
quizzes
Graded Programming Assignments
Capstone Project
Course Certificate.

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Coursera

The fees for the course Probabilistic Deep Learning with TensorFlow 2 is -

Head	Amount in INR
1 month	Rs. 4,117
3 month	Rs. 8,234
6 month	Rs. 12,352

Eligibility Criteria

The ‘Probabilistic Deep Learning with TensorFlow 2’ online certification course requires the students to know the fundamental aspects of machine learning, understand the deep learning domain, and the knowledge of probability and statistics.

Certificate qualifying details

The students of the ‘Probabilistic Deep Learning with TensorFlow 2’ certification by Coursera will receive a course certificate from the Imperial College London after completion of the online classes, graded quizzes, and graded programming assignments successfully at the end of the course.

What you will learn

Knowledge of deep learningProgramming skillsMathematical skillStatistical skills

The ‘Probabilistic Deep Learning with TensorFlow 2’ certification syllabus is created for the students to gain professional skills and knowledge with the probabilistic neural network, aspects of deep learning, concepts of a generative model, techniques involved in TensorFlow, and the Probabilistic Programming Language(PRPL). By the end of the training program, students will be able to form a variational autoencoder algorithm to make a generative model of a synthetic image dataset built by themselves.

Who it is for

Research Associate

The ‘Probabilistic Deep Learning with TensorFlow 2’ online certification course is designed for the students, research associates, and industry professionals of the domain who wish to enhance their knowledge with the concepts and techniques involved in deep learning with TensorFlow.

Admission Details

The registration process for the ‘Probabilistic Deep Learning with TensorFlow 2’ online classes is done through the course website as per the following,

Step 1: Find the course page using the link,

https://www.coursera.org/learn/probabilistic-deep-learning-with-tensorflow2

Step 2: Choose the ‘Enroll For Free’ option.

Step 3: Fill in the relevant details.

Step 4: Complete the registration and join the course.

Application Details

The applicants for the ‘Probabilistic Deep Learning with TensorFlow 2’ online program should enter their relevant details on the registration page such as name, email address, user name, and password of the Coursera account or can create a new account.
They can also sign in using the valid Google, Facebook, or Microsoft account IDs.