Brochure
Enquire- Intro to course
- Getting started with Google Cloud and Qwiklabs
Intermediate
Online
6 Weeks
Quick facts
| particular | details | |
|---|---|---|
|
Medium of instructions
English
|
Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
|
Course overview
The Introduction to TensorFlow certification course is thoroughly an online programme that is available for free for a specific period of time. The main focus of the programme is to make students aware of the Tensor flow. The training will revolve around the topics of datasets, designing and building of Tensorflow, and other features of the programme, etc. The course will take up to nineteen to twenty hours of learning.
The Introduction to TensorFlow online course is created for the students who want to learn from the course at their own pace so that they can take as much time as required to completely understand every topic. There are no last dates for the programme and it has no limitations with regards to attendance percentage or timings of attending the sessions.
The course provides an intermediate level of learning and requires no academic qualifications to get admitted into the course. Students also get an opportunity to become a certified user of the course and obtain a verified certificate when the Introduction to TensorFlow certification syllabus is completed by the participant. The language of the programme is set for English, however, participants who have a language barrier can use the subtitles that are available in Russian, Spanish, Portuguese (European), English, and French languages.
The highlights
- Intermediate level course
- 19 hours of learning
- Free of cost course
- Offered by Coursera
- Certificate available
- Flexible study timings
- Online learning
- 4 weeks programme
- No admission fee
Program offerings
- Assignments
- Practice exercise
- Discussion groups
- Video lectures
- Readings
- Quizzes
Course and certificate fees
- Students can opt for a free trial to get a preview of the course for 7 days.
TensorFlow on Google Cloud fees details
| Particulars | Fee Amount in INR |
| TensorFlow on Google Cloud - Audit course | Free |
| TensorFlow on Google Cloud - 1 month | Rs.4,115/- |
| TensorFlow on Google Cloud - 3 months | Rs.8,230/- |
| TensorFlow on Google Cloud - 6 months | Rs.12,345/- |
certificate availability
Yes
certificate providing authority
Coursera
Eligibility criteria
Certification Qualifying Details
Upon the successful completion of the entire lessons of the programme, students will be rewarded with certification at the end of the programme.
What you will learn
Machine learning
Knowledge of python
Students will come to acquire information regarding the following points by pursuing the Introduction to TensorFlow programme-
- Students will be able to learn the Keras functional and sequential API in the Introduction to TensorFlow certification.
- Students will acquire knowledge regarding the design and how to build a tensor flow 2.x input data pipeline.
- Candidates will come to know about the usage of tf.data library in order to manipulate data and large data sets.
- Participants will gain valuable insights into the production of ML models at large with cloud AI platforms.
- Candidates will learn about the Tensor flow API hierarchy in the Introduction to TensorFlow training.
- Students will come to know about the activation functions in the course.
- Learners will get an understanding of the regularisation and its aspects through the Introduction to TensorFlow programme.
Admission details
At the time of admission, the students can follow the instructions provided below-
Step 1- Participants need to visit the official website of the course.
Step 2- Students need to get enrolled in the programme.
Step 3- Students can click on the enrol button available on the page.
Step 4- Students need to provide their name and email address for signing up for the course.
Step 5- Students can click on the Go to course option to access the study material of the programme.
The syllabus
Week 1
Introduction to course
Introduction to Tensor flow
- Introduction to TensorFlow
- Components of TensorFlow: tensors and variables
- TensorFlow API Hierarchy
- Lab intro introduction to tensors and variables
- Lab Intro writing low-level TensorFlow programs
Introduction to course
- Intro to course
- Getting started with Google Cloud and Qwiklabs
Introduction to Tensor flow
- Introduction to TensorFlow
- Components of TensorFlow: tensors and variables
- TensorFlow API Hierarchy
- Lab intro introduction to tensors and variables
- Lab Intro writing low-level TensorFlow programs
Week 2
Design and build a tensor flow input data pipeline
- Overview
- Getting the data ready for model training
- Working in-memory and with files
- Lab intro load CSV and NumPy data
- Lab intro loading image data
- Lab intro feature columns
- Optional lab intro TFRecord and tf.Example
- Training on large datasets with tf.data API
- Lab intro manipulating data with Tensorflow dataset API
- Optional lab intro feature analysis using TensorFlow data validation and facets
Design and build a tensor flow input data pipeline
- Overview
- Getting the data ready for model training
- Working in-memory and with files
- Lab intro load CSV and NumPy data
- Lab intro loading image data
- Lab intro feature columns
- Optional lab intro TFRecord and tf.Example
- Training on large datasets with tf.data API
- Lab intro manipulating data with Tensorflow dataset API
- Optional lab intro feature analysis using TensorFlow data validation and facets
Week 3
Training neural networks with tensor flow 2 and the Keras sequential API
- Overview
- Activation functions
- Lab intro Keras sequential API
- Neural networks with keras sequential API
- Activation functions: pitfalls to avoid in backpropagation
Training neural networks with tensor flow 2 and the Keras sequential API
- Overview
- Activation functions
- Lab intro Keras sequential API
- Neural networks with keras sequential API
- Activation functions: pitfalls to avoid in backpropagation
Week 4
Training neural networks with tensor flow 2 and the Keras functional API
- Neural networks with Keras functional API
- Serving models in the cloud
- Regularization: the basics
- Regularization: L1, L2, and early stopping
- Regularization: dropout
- Lab intro Keras functional API
Training neural networks with tensor flow 2 and the Keras functional API
- Neural networks with Keras functional API
- Serving models in the cloud
- Regularization: the basics
- Regularization: L1, L2, and early stopping
- Regularization: dropout
- Lab intro Keras functional API
Scholarship Details
Students can attain the scholarship scheme only if they apply for financial aid for the programme.
How it helps
Students who register for the online course will be able to gain an understanding of topics related to Tensor flow. This intermediate level of the programme is offered for a time period of 7 days for free. Students will be able to complete the Introduction to TensorFlow certification syllabus if they dedicate approximately 19 to 20 hours of time to this course. The study materials for the course is already available in the programme and it includes readings, video notes, quizzes, assignments, discussion groups, etc.
At the end of the programme, when a student completes the whole programme, he or she will be able to qualify for the certification that benefits the candidate both- professionally and academically. The Introduction to TensorFlow certification benefits people who want to apply for an advanced level of training in the relevant field. Students who are looking for a job can pursue the programme and share the certificate on different social platforms such as LinkedIn. Students can also mention the same as a highlight in their CVs or resume.
Due to the market value associated with the certification, students will be able to increase their profile weightage through the achievement of the Introduction to TensorFlow certification course. If an already employed participant joins the programme, he or she can ask for salary raise in their respective companies.
Instructors
Google Cloud Training
Instructor
Google Cloud
FAQs
What is the time limitation for the programme?
Students have to complete the programme in 19 to 20 hours.
How many weeks will the Introduction to TensorFlow certification take?
The course needs a minimum of 4 weeks to complete the programme.
How will I get the certificate of completion?
Students will become entitled to the programme certification when they successfully complete the course.
When will the Introduction to TensorFlow training end?
Students can end the course in case they have completed the programme.
Does the course offer university credits for the programme?
No, the provision of providing credit equivalent to University credit is not available.
Is financial aid available for the programme?
Yes, financial aid is available for the programme.
How do I enter the course at no cost?
Students can enter the course for 7 days without paying any amount.
How can I obtain the certificate?
If the student is eligible for the certification, he or she can download the same from the Coursera platform.
What is the time limitation for the programme?
Students have to complete the programme in 19 to 20 hours.
How many weeks will the Introduction to TensorFlow certification take?
The course needs a minimum of 4 weeks to complete the programme.
How will I get the certificate of completion?
Students will become entitled to the programme certification when they successfully complete the course.
When will the Introduction to TensorFlow training end?
Students can end the course in case they have completed the programme.
Does the course offer university credits for the programme?
No, the provision of providing credit equivalent to University credit is not available.
Is financial aid available for the programme?
Yes, financial aid is available for the programme.
How do I enter the course at no cost?
Students can enter the course for 7 days without paying any amount.
How can I obtain the certificate?
If the student is eligible for the certification, he or she can download the same from the Coursera platform.
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