As artificial intelligence (AI) and machine learning (ML) have gained more and more traction, various powerful software libraries have developed in recent years. One such library that has become a common name among AI and ML developers is TensorFlow, which is developed by Google.
TensorFlow is an open-source software library for artificial intelligence and machine learning , and the best part - it’s completely free. Being accessible to everyone and supported by Google itself, this library has gained a lot of traction in a short span.
Now, if you want to become an AI or ML developer, it’s a great idea to learn TensorFlow and add it to your skill set. Leveraging TensorFlow makes it much simpler to create deep learning and machine learning models.
Many organizations today are searching for professionals who are well-versed with the implementation of TensorFlow. Thus, learning this library can prove beneficial for getting better job opportunities.
If you want to learn and master TensorFlow, you can consider taking a relevant course. Here, we will discuss the 10 best TensorFlow courses that can help you understand the software library effectively.
So without further ado, let’s get started!
10 Most Popular Tensorflow Courses to Take in 2023
Following are the best courses for learning TensorFlow this year:
1. TensorFlow Developer Certificate in 2023: Zero to Mastery
Highlights
- Platform: Udemy
- Level: Beginner
- Duration: 63 hours 30 minutes (approximately)
- Instructor: Andrei Neagoie and Daniel Bourke
- Certificate: Yes
Prerequisites
- A working computer system that has Windows, macOS, or Linux operating system.
- A fundamental understanding of machine learning would be advantageous.
Course Overview
If you are absolutely new to TensorFlow, this course available on Udemy can be a great option for you. It will teach you the fundamentals of TensorFlow right from scratch and also help you get familiar with various high-level concepts. Zero to Mastery is the creator of this course, and you will learn the course from two experts, namely Andrei Neagoie and Daniel Bourke.
There are a total of 19 sections in this TensorFlow course that covers a vast range of concepts. You will learn how to utilize TensorFlow for neural network regression, neural network classification, computer vision, and transfer learning. Also, the course introduces you to natural language processing (NLP) and how TensorFlow comes in handy for developing NLP models.
Key Highlights
- The course will assist you in preparing for the Google TensorFlow Developer Certificate exam.
- You will learn to build efficient machine learning models using TensorFlow 2.
- After completing the course, you will develop various key skills required to work as a professional TensorFlow Developer.
- The course includes 42 articles and 5 downloadable resources in addition to video lessons.
Sign up here to get started with the course.
2. Tensorflow 2.0: Deep Learning and Artificial Intelligence
Highlights
- Platform: Udemy
- Level: Beginner to Advanced
- Duration: 22 hours 13 minutes
- Instructor: Lazy Programmer Team
- Certificate: Yes
Prerequisites
- Knowledge of Python programming and NumPy library.
- Understanding of derivatives and probability will make it easier to learn the theoretical portion of the course.
Course Overview
This is a comprehensive TensorFlow course that is ideal for beginners as well as advanced learners. It covers a wide range of topics that are essential for developing a firm understanding of the TensorFlow library. Divided into 21 sections, the course offers 22 hours of on-demand video to facilitate effective learning. Also, you can access the course content on smartphones and tablets.
By taking the course, you will learn about Google Colab, machine learning, artificial neural networks (ANNs), convolutional neural networks (CNNs), recurrent neural networks, and natural language processing. Moreover, the course explains the theory of deep learning reinforcement and helps you become familiar with the advanced use of TensorFlow.
Key Highlights
- You can complete the course at your own pace.
- You will work on a stock trading project to strengthen your knowledge of deep reinforcement learning.
- It is one of the top-rated courses on Udemy with an average user rating of 4.7 stars out of 5.
To start learning the course, you can register here .
3. TensorFlow: Advanced Techniques Specialization
Highlights
- Platform: Coursera
- Level: Intermediate
- Duration: 5 months (7 hours/week)
- Instructor: Laurance Moroney and Eddy Shyu
-
Certificate:
Yes
Prerequisites
- Basic understanding of statistics, calculus, and linear algebra.
- Experience in Python programming.
- Knowledge of artificial intelligence and deep learning fundamentals.
Course Overview
TensorFlow: Advanced Techniques Specialization offered by Coursera can help you master TensorFlow. This is a highly popular specialization with an average user rating of 4.8 stars out of 5. Also, more than 15K individuals have enrolled in this specialization.
You can also enroll in TensorFlow: Advanced Techniques Specialization if you are looking to get in-depth knowledge of advanced TensorFlow techniques, including style transfer, object detection, and generative machine learning.
Offered by DeepLearning.AI, it combines 4 different courses that are namely Custom Models, Layers, and Loss Functions with TensorFlow, Custom and Distributed Training with TensorFlow, Advanced Computer Vision with TensorFlow, and Generative Deep Learning with TensorFlow.
Key Highlights
- You will learn how to build non-sequential model types and custom loss functions.
- It requires you to work on a hands-on project to accomplish the specialization.
- The course offers a flexible schedule.
You can enroll in the course here .
4. TensorFlow 2 for Deep Learning Specialization
Highlights
- Platform: Coursera
- Level: Intermediate
- Duration: 4 months (7 hours/week)
- Instructor: Dr. Kevin Webster
- Certificate: Yes
Prerequisites
- Hands-on experience with Python 3.
- Basic knowledge of machine learning and deep learning concepts.
- Familiarity with probability and statistics.
Course Overview
Coursera brings you this specialization to develop a comprehensive understanding of TensorFlow. Developed by Imperial College of London, this specialization is a combination of three courses, namely Getting Started with TensorFlow 2, Customizing models with TensorFlow2, and Probabilistic Deep Learning with TensorFlow 2.
The instructor for this course is Dr. Kevin Webster who works at Imperial College of London as a Senior Teaching Fellow in the Department of Mathematics.
This specialization is a perfect choice for machine learning developers who want to create high-performance deep learning models by using TensorFlow. From fundamentals concepts required to build and train simple deep learning models to advanced concepts that facilitate the development of customized models, the courses in this specialization cover a lot of important topics.
Key Highlights
- The course will help you learn about TensorFlow APIs.
- Each course requires you to work on a capstone project to enhance your practical knowledge.
- The video lessons come with subtitles in different languages, including Arabic, Italian, Korean, and German.
To get started with the course, you can sign up here .
5. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Highlights
- Platform: Coursera (DeepLearning.AI)
- Level: Intermediate
- Duration: 18 hours (approximately)
- Instructor: Laurance Moroney
- Certificate: Yes
Prerequisites
- Experience in Python programming .
- Knowledge of high school-level math.
Course Overview
This is yet another popular TensorFlow course that makes a perfect choice if you want to develop powerful AI algorithms. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning is a course for intermediate learners having experience in Python programming.
The course is offered by DeepLearning.AI, which is a popular education technology company. Also, Laurence Moroney is the instructor for this course and is a Lead AI Advocate at Google. The course has four weekly modules with each module being a combination of video lessons, readings, and quizzes.
You will get a brief introduction to machine learning and deep learning at the start of the course. After that, you will learn topics like computer vision, convolutions, and pooling. Additionally, you will use TensorFlow to create a neural network for a computer vision application.
Key Highlights
- You will understand how you can utilize TensorFlow to develop solutions for real-world problems.
- You can enroll in the course for free.
Register for the course here to start learning.
6. Complete Guide to TensorFlow for Deep Learning with Python
Highlights
- Platform: Udemy
- Level: Intermediate
- Duration: 14 hours (approximately)
- Instructor: Jose Portilla
- Certificate: Yes
Prerequisites
- Basic knowledge of programming.
- Understanding of basic mathematics concepts, such as mean and standard deviation.
Course Overview
As we all know, TensorFlow is a powerful deep learning framework. However, mastering this framework requires you to gain an in-depth knowledge of its various concepts, and this Udemy course is going to exactly help you with that.
Complete Guide to TensorFlow for Deep Learning with Python is among the most popular TensorFlow courses available on Udemy. Jose Portilla, who is the head of Data Science at Pierian Training, is the creator of this detailed course.
By taking this course, you will learn the different aspects of TensorFlow that make it an ideal choice for deep learning. To complete the course, you need to go through its 12 sections that cover topics including machine learning, TensorFlow basics, convolution neural networks, and recurrent neural networks. Additionally, you will learn how to solve unsupervised learning problems by using TensorFlow.
Key Highlights
- The course explains the working of neural networks.
- You will learn about OpenAI gym, which is an environment to facilitates reinforcement learning.
- The course will guide you in developing a neural network using Python.
Start enrolling in the course here .
7. Getting Started with Tensorflow 2.0
Highlights
- Platform: Pluralsight
- Level: Beginner
- Duration: 3 hours 10 minutes (approximately)
- Instructor: Janani Ravi
- Certificate: Yes
Prerequisites
- A working computer system and understanding of computer fundamentals.
Course Overview
This course by Pluralsight deserves to be on our list of the best TensorFlow courses because it is utterly suitable for beginners who want to learn TensorFlow 2.You may finish the introduction course in only three hours.
Also, you can make progress in the course at your own pace and there is no defined timeline. Janani Ravi will teach you this course, and she has worked for multiple tech giants, including Google, Microsoft, and Flipkart.
Getting Started with Tensorflow 2.0 has 6 modules that cover a variety of topics related to TensorFlow 2. Also, the course explains the key differences between TensorFlow 1 and TensorFlow2.
You will develop an in-depth understanding of static and dynamic computation graphs, computing gradients, and the sequential API in Keras. At the end of the course, you will feel confident working with TensorFlow 2 to solve real-world problems.
Key Highlights
- The course introduces you to Keras, which is a deep learning API for Python.
- You will learn the concepts through video lessons.
- You can access the course's content from any location using any internet-capable device because it is entirely online.
You can sign up for the course here .
8. Intro to TensorFlow for Deep Learning
Highlights
- Platform: Udacity
- Level: Intermediate
- Duration: 2 months
- Certificate: Yes
Prerequisites
- Basic knowledge of Python programming.
- Knowledge of algebra.
Course Overview
This is another best TensorFlow course offered by Udacity. This course will teach you how to create deep learning applications using TensorFlow. Also, you’ll get familiar with the fundamentals of deep learning, supervised learning and unsupervised learning . The best thing about this course is that it is developed by Udacity in collaboration with the TensorFlow team. Thus, you can expect a great learning experience.
There are 9 lessons in this course that cover topics such as convolutional neural networks (CNNs), transfer learning, natural language processing, and time series forecasting. Additionally, you will develop a neural network from scratch that is capable of recognizing images of articles of clothing.
Key Highlights
- The course will introduce you to TensorFlow Lite.
- You will learn TensorFlow better by doing exercises.
- The course is taught by three experts.
- You can take this course for free.
Register here to get started with the course.
9. Deploying TensorFlow Models to AWS, Azure, and the GCP
Highlights
- Platform: Pluralsight
- Level: Intermediate
- Duration: 2 hours 11 minutes
- Instructor: Janani Ravi
- Certificate: Yes
Prerequisites
- Experience in Python programming and TensorFlow.
- Basic knowledge of Google Cloud Platform, AWS, and Azure .
Course Overview
This popular TensorFlow course by Pluralsight can help you get familiar with the process of deploying a TensorFlow model on a popular cloud platform like Azure, AWS, or GCP. This course is specifically intended for professionals who are capable of building machine learning and deep learning models with TensorFlow. Thus, if you are a complete beginner, this course is not for you.
Janani Ravi is the author of Deploying TensorFlow Models to AWS, Azure, and the GCP, and she has authored various other TensorFlow courses. She has divided this course into 5 different modules that will guide you through the TensorFlow model deployment process for each Microsoft Azure, Amazon AWS, and Google Cloud Platform.
Key Highlights
- The course is fully online and self-paced.
- You will learn the course through video lessons.
- You can try the course by opting for the 10-day free trial offered by Pluralsight.
To start learning the course, you can enroll here .
10. Deep Learning with Tensorflow
Highlights
- Platform: edX
- Level: Intermediate
- Duration: 5 weeks (2-4 hours/week)
- Instructor: Saeed Aghabozorgi, Romeo Kienzler, and Samaya Madhavan
- Certificate: Yes
Prerequisites
- Experience in Python programming.
- Basic understanding of machine learning and deep learning .
Course Overview
Deep Learning with Tensorflow is the last entry on our list of the best TensorFlow courses. This course is offered by IBM and is available on the edX platform. This is one of the most desirable courses for individuals who want to learn and master TensorFlow for deep learning.
If you enroll in this course, you will learn the implementation of TensorFlow from three professional IBM employees, namely Saeed Aghabozorgi (Sr. Data Scientist), Romeo Kienzler (Chief Data Scientist), and Samaya Madhavan (Advisory Software Engineer).
By taking this course, you will learn the fundamentals of TensorFlow that are essential for working with artificial neural networks. Also, you will understand how you can use TensorFlow for regression, curve fitting, and minimization of error functions.
Key Highlights
- The course introduces you to convolutional neural networks, recurrent neural networks, and autoencoders.
- You can take this course for free.
You can sign up for the course here .
Conclusion
Learning the TensorFlow library can be a good decision if you are interested in artificial intelligence, machine learning, and deep learning.
Basically, TensorFlow lets you build and train deep learning models as well as machine learning models. Moreover, there are several other ways in which this popular library can come in useful to you as a professional AI or ML developer.
If you want to learn and see how TensorFlow helps to solve various real-world problems, you can enroll in one of the best TensorFlow courses mentioned above.
However, keep in mind that each course has different contents and pricing; some are available for free while others require you to pay a fee. Thus, you must choose a TensorFlow course that suits your requirements and budget.
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