Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.
This course is part of the TensorFlow: Data and Deployment Specialization
Offered By
TensorFlow: Data and Deployment Specializationdeeplearning.aiAbout this Course
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Shareable Certificate
Earn a Certificate upon completion
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Start instantly and learn at your own schedule.
Course 3 of 4 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have a basic familiarity with building models in TensorFlow
Approx. 19 hours to complete
English
Subtitles: English
What you will learn
Leverage built-in datasets with just a few lines of code
Use APIs to control how you split your data
Process all types of unstructured data
Skills you will gain
TensorflowMachine Learning
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 3 of 4 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have a basic familiarity with building models in TensorFlow
Approx. 19 hours to complete
English
Subtitles: English
Offered by
deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
Syllabus - What you will learn from this course
5 hours to complete
Data Pipelines with TensorFlow Data Services
You'll learn about the types of data that you would normally come across when doing machine learning.
5 hours to complete
14 videos (Total 27 min), 2 readings, 2 quizzes
14 videos
Introduction1m
Popular datasets2m
Data pipelines58s
Extract, transform, load3m
Versioning datasets2m
Looking at the notebook1m
Introduction43s
Legacy API and Subsplits5m
Splits API (S3)2m
Introduction22s
Legacy API in code1m
Splits API (S3) in code1m
Week 1 wrap up43s
2 readings
Downloading the Coding Examples and Exercises10m
Try out the notebook yourself10m
1 practice exercise
Week 1 Quiz
6 hours to complete
Exporting your data into the training pipeline
This week you’re going to start looking at the code for using the data with input pipelines!
6 hours to complete
21 videos (Total 44 min), 5 readings, 2 quizzes
21 videos
Introduction22s
Input data1m
Basic mechanics2m
Numeric and bucketized columns2m
Vocabulary and hashed columns, feature crossing2m
Embedding columns2m
Introduction24s
Notebook walkthrough4m
Introduction19s
Numpy, Pandas and Images2m
CSV3m
Text and TFRecord1m
Generators1m
Introduction17s
Notebook walkthrough4m
Introduction1m
Numpy and Pandas2m
Images1m
CSV4m
Text2m
5 readings
Link to the notebook10m
Link to the CNN course10m
Link to the notebook10m
CSV: colab10m
Link to the tokenization10m
1 practice exercise
Week 2 Quiz
4 hours to complete
Performance
How you load your data into your model can have a huge impact on how efficiently the model trains. You'll learn how to handle your data input to avoid bottlenecks, race conditions and more!
4 hours to complete
11 videos (Total 20 min)
11 videos
Introduction36s
ETL2m
What happens when you train a model2m
Introduction25s
Caching58s
Parallelism APIs2m
Autotuning2m
Parallelizing data extraction2m
Best practices for code improvements3m
A few words by Laurence34s
1 practice exercise
Week 3 Quiz
5 hours to complete
Publishing your datasets
This week let’s learn about how you can share your data with the world in a way that’s easy for others to consume!
5 hours to complete
11 videos (Total 24 min), 2 readings, 2 quizzes
11 videos
Introduction44s
How to start using a dataset2m
Implementation4m
File access and possible problems in data3m
Publishing the dataset3m
Introduction18s
Going through the colab (1)2m
Going through the colab (2)2m
Closing words14s
A conversation with Andrew Ng1m
2 readings
URLs10m
Link to the colab10m
1 practice exercise
Week 4 Quiz
About the TensorFlow: Data and Deployment Specialization
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models.
In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more.
Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever.
This Specialization builds upon skills learned in the TensorFlow in Practice Specialization. We recommend learners complete that Specialization prior to enrolling in TensorFlow: Data and Deployment.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
More questions? Visit the Learner Help Center.
