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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 Imperial College of London offers the Getting Started With TensorFlow 2 online course in conjunction with Coursera. Achieving certification in courses offered by this esteemed institution gives you an inclusive educational experience and superior digital technology.

The Getting Started With TensorFlow 2 certification course has modules spread out over five weeks, which candidates can complete in approximately 26 hours. Dr Kevin Webster, a Senior Teaching Fellow in Statistics at the Imperial College London, instructs the certification course.

An intermediate-level course, the Getting Started With TensorFlow 2 training will teach you the fundamentals concepts of TensorFlow. You will become fully adept in developing deep learning models and using them to evaluate, train, and make predictions.

Furthermore, the Getting Started With TensorFlow 2 certification course promotes interaction and improvement of one’s core abilities with a comprehensive curriculum and intermittent assignments. You can self-assess through these mediums and grasp the course contents at your own pace. Finally, Coursera awards you with a certificate upon successful completion.

The Highlights

100% online
Shareable certificate
Course videos
Readings
Intermediate level course
Financial Aid available
Lectures in English and subtitles available in English
Fast completion (approximately 26 hours)
Flexible deadlines
Capstone project

Programme Offerings

Course completion certificate
discussion forum
Email Updates and Communication
26 Hours learning
Shareable Certificate
Graded Programming Assignments

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Coursera

Getting Started With TensorFlow 2 fees details:

Particulars	Amount
Course Fee, 1 Month	Rs. 4,023
Course Fee, 3 Month	Rs. 8,046
Course Fee, 6 Month	Rs. 12,069

Eligibility Criteria

For satisfactory completion of the Getting Started With TensorFlow 2 certification by Coursera course, candidates need to know the fundamentals of python 3. Candidates should also have proficiency in the basics of machine learning and related concepts like validation, model selection, and more. Additionally, they should have a complete understanding of deep learning fundamentals.

What you will learn

Machine learningKnowledge of deep learning

Upon completion of all the modules in the Getting Started With TensorFlow 2 certification syllabus, you will be able to:

Develop more in-depth knowledge in the field of machine learning
Build deep learning models with the help of TensorFlow
Use Sequential API to build, train, predict and examine deep learning models
Load and save models as well as the implementation of callbacks
Apply the concepts with coding tutorials

Who it is for

Machine Learning Engineer

The Getting Started With TensorFlow 2 course by Coursera is for the following professionals:

Deep Learning Engineers and students
Machine Learning Engineers and aspiring students

Admission Details

To apply for the Getting Started With TensorFlow 2 classes:

Step 1. Go to the Coursera website- https://www.coursera.org/

Step 2.Find ‘Getting Started With TensorFlow 2’.

Step3. Click on the ‘Enrol for Free’ option.

Step 4. Follow the instructions and enroll for the course. You can sign up using your email ID or Google account to start learning.

Application Details

Use your gmail or another Google account to sign up for the TensorFlow 2 Coursera course. You can choose the enrollment type you prefer and access course materials and certificate accordingly.

The Syllabus

Videos

Introduction to the course
Welcome to week 1
Hello TensorFlow!
[Coding tutorial] Hello TensorFlow!
What's new in TensorFlow
Interview with Laurence Moroney
Introduction to Google Colab
[Coding tutorial] Introduction to Google Colab
TensorFlow documentation
TensorFlow installation
[Coding tutorial] pip installation
[Coding tutorial] Running TensorFlow with Docker
Upgrading from TensorFlow
[Coding tutorial] Upgrading from TensorFlow 1

Readings

About Imperial College & the team
How to be successful in this course
Grading policy
Additional readings & helpful references
What is TensorFlow?
Google Colab resources
TensorFlow documentation
Upgrade TensorFlow 1.x Notebooks

Videos

Welcome to week 2 - The Sequential model API
What is Keras?
Building a Sequential model
[Coding tutorial] Building a Sequential model
Convolutional and pooling layers
[Coding tutorial] Convolutional and pooling layers
The compile method
[Coding tutorial] The compile method
The fit method
[Coding tutorial] The fit method
The evaluate and predict methods
[Coding tutorial] The evaluate and predict methods
Wrap up and introduction to the programming assignment

Practice exercises

[Knowledge check] Feedforward and convolutional neural networks
[Knowledge check] Optimisers, loss functions and metrics

Videos

Welcome to week 3 - Validation, regularisation and callbacks
Interview with Andrew Ng
Validation sets
[Coding Tutorial] Validation sets
Model regularisation
[Coding Tutorial] Model regularisation
Introduction to callbacks
[Coding tutorial] Introduction to callbacks
Early stopping and patience
[Coding tutorial] Early stopping and patience
Wrap up and introduction to the programming assignment

Practice exercise

[Knowledge check] Validation and regularisation

Videos

Welcome to week 4 - Saving and loading models
Saving and loading model weights
[Coding tutorial] Saving and loading model weights
Model saving criteria
[Coding tutorial] Model saving criteria
Saving the entire model
[Coding tutorial] Saving the entire model
Loading pre-trained Keras models
[Coding tutorial] Loading pre-trained Keras models
TensorFlow Hub modules
[Coding tutorial] TensorFlow Hub modules
Wrap up and introduction to the programming assignment

Videos

Welcome to the Capstone Project
Goodbye video

Instructors

Imperial College, London Frequently Asked Questions (FAQ's)

1: When will I start getting access to assignments and lectures?

It depends on the type of enrollment you opted for. If you take a course in audit mode, you will get access to maximum course materials for free. Those who want access to graded assignments and earn a Certificate, need to purchase the Certificate experience. This can be done during, or after your audit.

2: What are the benefits offered to candidates who subscribe to the Specialization?

Paid subscribers get access to all the courses that are a part of the Specialization. On successful completion of the course, Coursera will add your electronic Certificate to your Accomplishments page. Candidates can print the Certificate or add it to their LinkedIn profile.

3: Is it possible to get financial aid?

Yes, Coursera offers financial support to candidates who cannot pay the fee for the Getting Started With TensorFlow 2 course. You can apply for financial assistance by clicking on the link that is below the enrol button on the left. Once you are approved, Coursera will notify you.

4: Will I get any university credit after completing the course?

The Getting Started With TensorFlow 2 course does not offer university credit. However, some universities may accept Course Certificates for credit. You can get in touch with your university to confirm the same and accordingly submit the course certificate.

5: Are there weekly deadlines for the Getting started with TensorFlow 2 online course modules?

This is a flexible course where one can easily reset the deadline and complete the assignments as well as lectures at their own pace.