Introduction to TensorFlow faq

instructor Instructor: Google Cloud instructor-icon
duration Duration: 4.00 duration-icon

This course provides an introduction to TensorFlow, a powerful tool for creating machine learning models. Students will learn how to use the TensorFlow libraries to solve numerical problems, debug common errors, and use the Estimator API to create, train, and evaluate ML models. Finally, they will learn how to deploy and productionalize ML models at scale with Cloud AI Platform.

ADVERTISEMENT

Course Feature Course Overview Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Free Trial

providerProvider:

Pluralsight

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

On-Demand

Course Overview

❗The content presented here is sourced directly from Pluralsight platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [March 06th, 2023]

This course provides an introduction to TensorFlow, a powerful tool for creating machine learning models. Students will learn how to use the TensorFlow libraries to solve numerical problems, as well as how to identify and fix common errors. The Estimator API, which provides the highest level abstraction within TensorFlow for training, evaluating and serving machine learning models, will also be covered. Students will learn how to use tf_estimator to create, train, and evaluate an ML model. Finally, students will learn how to execute TensorFlow models on Cloud AI Platform, Google-managed infrastructure to run TensorFlow, and how to train, deploy, and productionalize ML models at scale with Cloud AI Platform.

[Applications]
Upon completion of this course, students should be able to apply the knowledge they have gained to create and deploy machine learning models in TensorFlow. They should be able to use the Estimator API to create, train, and evaluate ML models, as well as execute TensorFlow models on Cloud AI Platform. Additionally, they should be able to productionalize ML models at scale with Cloud AI Platform.

[Career Paths]
The career paths recommended to learners of this course are:

1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models using TensorFlow. They must have a strong understanding of the TensorFlow libraries and be able to use them to solve numerical problems. They must also be able to use the Estimator API to create, train, and evaluate ML models. The demand for Machine Learning Engineers is increasing as more companies are looking to leverage the power of machine learning to improve their products and services.

2. Data Scientist: Data Scientists use TensorFlow to analyze large datasets and uncover insights. They must have a strong understanding of the TensorFlow libraries and be able to use them to solve numerical problems. They must also be able to use the Estimator API to create, train, and evaluate ML models. Data Scientists are in high demand as companies are looking to leverage the power of data to make better decisions.

3. Cloud AI Platform Engineer: Cloud AI Platform Engineers are responsible for deploying and managing TensorFlow models on Cloud AI Platform. They must have a strong understanding of the TensorFlow libraries and be able to use them to solve numerical problems. They must also be able to use the Estimator API to create, train, and evaluate ML models. Cloud AI Platform Engineers are in high demand as companies are looking to leverage the power of cloud computing to scale their machine learning models.

4. AI Developer: AI Developers are responsible for developing AI applications using TensorFlow. They must have a strong understanding of the TensorFlow libraries and be able to use them to solve numerical problems. They must also be able to use the Estimator API to create, train, and evaluate ML models. AI Developers are in high demand as companies are looking to leverage the power of AI to create innovative applications.

[Education Paths]
The education paths recommended to learners of this course are:

1. Bachelor of Science in Computer Science: This degree path provides students with a comprehensive understanding of computer science fundamentals, including programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, machine learning, and natural language processing. This degree path is ideal for those interested in developing and deploying machine learning models using TensorFlow.

2. Master of Science in Artificial Intelligence: This degree path provides students with a deeper understanding of artificial intelligence and machine learning, including topics such as deep learning, reinforcement learning, and natural language processing. It also covers topics such as computer vision, robotics, and autonomous systems. This degree path is ideal for those interested in developing and deploying advanced machine learning models using TensorFlow.

3. Master of Science in Data Science: This degree path provides students with a comprehensive understanding of data science fundamentals, including data mining, data analysis, and data visualization. It also covers topics such as machine learning, artificial intelligence, and natural language processing. This degree path is ideal for those interested in developing and deploying machine learning models using TensorFlow.

4. Doctor of Philosophy in Machine Learning: This degree path provides students with a comprehensive understanding of machine learning fundamentals, including topics such as deep learning, reinforcement learning, and natural language processing. It also covers topics such as computer vision, robotics, and autonomous systems. This degree path is ideal for those interested in developing and deploying advanced machine learning models using TensorFlow.

The developing trends in these degree paths include the use of more advanced machine learning algorithms, the use of cloud computing for training and deploying models, and the use of natural language processing for understanding and interpreting data. Additionally, the use of deep learning and reinforcement learning is becoming increasingly popular in the field of machine learning.

Course Provider

Provider Pluralsight's Stats at AZClass

Pluralsight ranked 16th on the Best Medium Workplaces List.
Pluralsight ranked 20th on the Forbes Cloud 100 list of the top 100 private cloud companies in the world.
Pluralsight Ranked on the Best Workplaces for Women List for the second consecutive year.
AZ Class hope that this free trial Pluralsight course can help your Tensorflow skills no matter in career or in further education. Even if you are only slightly interested, you can take Introduction to TensorFlow course with confidence!

learners

31,000 Learners

courses

7,000 Courses

Discussion and Reviews

0.0   (Based on 0 reviews)

Start your review of Introduction to TensorFlow

Quiz

submit successSubmitted Sucessfully

1. What is the Estimator API?

2. What is the purpose of the TensorFlow libraries?

3. What is the goal of the course?

close
part

faq FAQ for Tensorflow Courses

Q1: Does the course offer certificates upon completion?

Yes, this course offers a free trial certificate. AZ Class have already checked the course certification options for you. Access the class for more details.

Q2: How do I contact your customer support team for more information?

If you have questions about the course content or need help, you can contact us through "Contact Us" at the bottom of the page.

Q3: How many people have enrolled in this course?

So far, a total of 0 people have participated in this course. The duration of this course is 4.00 hour(s). Please arrange it according to your own time.

Q4: How Do I Enroll in This Course?

Click the"Go to class" button, then you will arrive at the course detail page.
Watch the video preview to understand the course content.
(Please note that the following steps should be performed on Pluralsight's official site.)
Find the course description and syllabus for detailed information.
Explore teacher profiles and student reviews.
Add your desired course to your cart.
If you don't have an account yet, sign up while in the cart, and you can start the course immediately.
Once in the cart, select the course you want and click "Enroll."
Pluralsight may offer a Personal Plan subscription option as well. If the course is part of a subscription, you'll find the option to enroll in the subscription on the course landing page.
If you're looking for additional Tensorflow courses and certifications, our extensive collection at azclass.net will help you.

close

To provide you with the best possible user experience, we use cookies. By clicking 'accept', you consent to the use of cookies in accordance with our Privacy Policy.