Machine Learning Projects with TensorFlow 20 faq

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4.4
learnersLearners: 87
instructor Instructor: Packt Publishing instructor-icon
duration Duration: duration-icon

Learn how to build and deploy machine learning projects with TensorFlow 20, and gain the skills to solve real-world problems with confidence, thanks to the proven Problem Promise Proof Proposal method.

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

Course Feature

costCost:

Paid

providerProvider:

Udemy

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

2020-05-04

Course Overview

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

Updated in [August 31st, 2023]

What does this course tell?
(Please note that the following overview content is from Alison)
This course will guide you to upgrade your skills in Machine Learning by building real-world projects with TensorFlow 2. You will learn how to implement various Machine Learning techniques and algorithms using the TensorFlow 2 library, and cover new features such as Eager Execution. You will also cover tasks such as Reinforcement Learning and Transfer Learning. By the end of the course, you will be confident to build your own Machine Learning Systems with TensorFlow 2 and will be able to add this valuable skill to your CV. The instructor, Vlad Ionescu, is a lecturer at Babes-Bolyai University with a PhD in Machine Learning and experience teaching various university-level courses and tutorials.

We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
What skills and knowledge will you acquire during this course?
By taking this course, students will acquire skills and knowledge in the areas of Machine Learning, Deep Learning, TensorFlow 2, Eager Execution, Reinforcement Learning, Transfer Learning, Python, Keras, C#, Java, algorithms, and data structures. They will also gain an understanding of how to build their own Machine Learning Systems with TensorFlow 2 and how to apply their skills to real-world scenarios. Additionally, they will learn tips and tricks to become more efficient in their work.
lHow does this course contribute to professional growth?
This course contributes to professional growth by providing learners with the opportunity to upgrade their skills in Machine Learning by practically applying them by building real-world Machine Learning projects. Learners will gain hands-on experience with the new features of TensorFlow 2, such as Eager Execution, and will be able to implement various Machine Learning techniques and algorithms using the TensorFlow 2 library. By the end of the course, learners will be confident to build their own Machine Learning Systems with TensorFlow 2 and will be able to add this valuable skill to their CV.

Is this course suitable for preparing further education?
Yes, this course is suitable for preparing further education as it covers various topics related to Machine Learning and Deep Learning, such as Eager Execution, Reinforcement Learning, Transfer Learning, and more. It also provides practical projects to help learners understand and apply the concepts they learn. Additionally, the course is taught by an experienced lecturer with a PhD in Machine Learning, who has a wealth of knowledge and experience in the field.

Course Syllabus

Regression Task Airbnb Prices in New York

Classification Task Build Real World Apps: Who Will Win the Next UFC?

Natural Language Processing Task: How to Generate Our Own Text

Reinforcement Learning Task: How to Become Best at Pacman

Transfer Learning Task: How to Build a Powerful Image Classifier

Course Provider

Provider Udemy's Stats at AZClass

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faq FAQ for Tensorflow Courses

Q1: Does the course offer certificates upon completion?

Yes, this course offers a paid 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 87 people have participated in this course. The duration of this course is 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 Udemy'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."
Udemy 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.

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