10 Best TensorFlow Courses [Beginner's to Intermediate Programmers]

Researchers developed Tensorflow at Google Brain to tackle the tasks associated with Neural Networks. It is a powerful software library that has quickly gained recognition for making the tasks of Machine Learning a lot simpler. Companies worldwide are looking for developers and engineers who can work with Tensorflow and create more realistic deployment models.

We have compiled a list of a few courses that will get you started on the path of technological advancement and help you reach your goal.

10 Best Tensorflow Courses

1. Tensorflow2 for Deep Learning Specialization

This course addresses researchers and developers of Machine Learning who wish to use Tensorflow to enhance their programming skills. It teaches the principles of Deep Learning and Neural Networks that assist in creating more genuine models.

The course covers topics such as:

• Introduction to Tensorflow and its utilities.
• Learning to customize models using the Tensorflow 2.0 library.
• Basics of Machine learning.
• Fundamentals of Deep Learning.
• Introduction to Neural networks.
• Working with Convolutional Neural Networks.
• Using Recursive Neural networks.
• Using Probabilistic Deep learning in Tensorflow to validate models.
• Learning to change variables and conceptualize models.
• Practicing the functions in Capstone Models.

On completing the course, students can apply for core roles in the programming process. They can create models at an advanced level for monetary benefit as well as the advancement of science.

Prerequisites: The website does not mention any prerequisites.

Level: Intermediate

Rating: 4.9

Duration: 4 months

You can sign up here

2. Getting started with Tensorflow2

This course provides an end-to-end base in the process of designing Deep Learning models with Tensorflow. The course is centered on gaining fluency in the practical application of Deep Learning Technology.

The course covers topics such as:

• Principles of Tensorflow.
• Learning about resources that assist in the development of Deep Learning Models.
• Learning Google Colab.
• Learning to use APIs to create models in Deep learning.
• Building and Training Deep Learning Models.
• Evaluating and predicting outcomes based on Deep Learning Models.
• Practicing the creation of image classification models.
• Using the MNIST dataset for analysis of handwritten images.
• Learning the process of validating models.
• Learning to implement regularization techniques.
• Using the Iris dataset for validation.
• Learning to save and load models.
• Creating flexible models.
• Implementing programming modifications on models trained on satellite images.
• Implementing all the principles in a capstone project to evaluate your understanding of the subject.

Students can earn certification and use it in their professional circles to advance their careers on completing the course. They can learn to practically create models with efficient end-to-end functions to tackle actual problems.

Prerequisites: The website does not mention any prerequisites.

Level: Intermediate

Rating: 4.9

Duration: 26 hours

You can sign up here

3. Introduction to Tensorflow for Artificial Intelligence, Machine Learning, and Deep Learning

This course teaches the open-source framework required to create models in Machine learning. This is a big part of learning to create AI-powered algorithms for real-world applications. It takes students from a beginner level to an advanced level in order to create state-of-the-art designs.

The course covers topics such as:

• Introduction to Deep Learning.
• Basics of Machine Learning.
• Fundamentals of TensorFlow.
• Concepts and conditioning in Tensorflow.
• Creating computer vision through coding.
• Learning the principles and applications of Convolutional Neural Networks.
• Analyzing Deep Neural Networks.
• Training models for real-time imaging.
• Learning to tackle complex images.

On completing the course, students can acquire a complete understanding of Deep Learning and Neural Networks. They can use this knowledge to create their models for independent deployment. They can also apply for jobs in Neural Programming and change the face of technology.

Prerequisites: The website does not mention any prerequisites.

Level: Intermediate

Rating: 4.7

Duration: 30 hours

You can sign up here

4. Tensorflow 2.0: Deep Learning and Artificial Intelligence

This course takes an ultimate tour of Neural Networks for diverse functions and teaches students to implement them to solve problems. It is a guided course, indulging in the methods of programming everything from basic fundamentals to advanced technologies.

The course covers topics such as:

• Working with Google Colab.
• Fundamentals of Machine Learning.
• Learning the classification Theory to understand how code is prepared.
• Learning to use regression.
• Fundamentals of Neurons and Neural Networks.
• Learning to make Predictions.
• Working with Forward Propagation.
• Learning to activate functions in programming.
• Using classification and Regression in Artificial Neural Networks.
• Fundamentals and implementation of Convolutional Neural Networks.
• Learning Data Augmentation.
• Using Recurrent Neural Networks.
• Working with Time Series
• Segregating sequential data.
• Preparing code for Natural Language Processing.
• Using Recommender systems.
• Working with Generative Adversarial Networks.
• Practicing Deep Reinforcement Learning.
• Participate in a stock trading project.
• Fundamentals of Advanced Tensorflow.

On completing the course, students can apply for a career in programming Neural Networks. They can work with analysis and diverse Neural Language functions. They can also expect more crucial roles in their organization due to their upgraded potential.

Prerequisites: Coding experience with Python and Numpy; Theoretical understanding of derivatives and probability.

Level: Intermediate

Rating: 4.6

Duration: 21 hours and 12 minutes

You can sign up here

5. Tensorflow Deep Learning - Data Science in Python

This course teaches programming with Tensorflow using Deep learning in Python. It gives a thorough insight into Neural Network training and creating models applicable to Java in Android.

The course covers topics such as:

• Learning the basics of Python data science environment.
• Fundamentals of Tensorflow and how it can be used in programming.
• Using Placeholders, variables, and constants in Tensorflow.
• Basics of Numpy and how to use it in operations.
• Learning Correlation Analysis in statistical modeling.
• Practicing multiple regressions with Tensorflow.
• Learning the functions of Binary Classifications.
• Fundamentals of Machine Learning.
• Types of Supervised and unsupervised learning and how to implement them.
• Working with Artificial Neural Networks.
• Working with Deep Learning in Tensorflow.
• Using Convolutional Neural Networks.

On completing the course, students will acquire advanced skills in the field of Deep Learning with Tensorflow. They can design models and apply these theories for state-of-the-art projects. They can proceed towards career advancements with an in-depth understanding of Neural Networks.

Prerequisites: Basic understanding of Python, Machine Learning terminology and Statistical concepts.

Level: Intermediate

Rating: 4.5

Duration: 7 hours and 9 minutes

You can sign up here

6. Introduction to Machine Learning with Tensor Flow

TensorFlow is much in demand in the ML field. This Nanodegree program offered by Udacity teaches the foundational techniques of machine learning with TensorFlow. The candidate gets the hands-on experience of every algorithm in TensorFlow and scikit-learn. Udacity provides much support for the students while enrolled in the program; the instructors will be in direct touch via live sessions to help understand the complex topics.  

Topics Covered

• Fundamentals of Machine Learning.
• Data Manipulation.
• Supervised Algorithms.
• Neural Networks with TensorFlow. 

After successfully completing the nanodegree program with given projects during the course, the candidate would get a certificate of completion. 

Prerequisites: Experience in Python.

Level: Intermediate

Rating: 4.5

Duration: 3 months 10hrs/week

You can sign up here

7. Tensorflow: Data and Deployment Specialization

This course teaches the various deployment scenarios and how to improve upon them using Tensorflow. It also teaches accurate measures to train a Machine learning Model to accomplish a task's specific roles. This is a specialization course. Therefore it takes a more in-depth path into the world of Machine learning.

The course covers topics such as:

• Using Tensorflow.js to train and navigate in Machine learning models on a browser.
• Learning to create and deploy a model that can classify objects through a webcam.
• Deploying and using Machine learning models in mobile applications.
• Creating models that run on low power or battery-operated devices.
• Learning to deploy models on embedded systems using Tensorflow.
• Using Tensorflow to perform ETL tasks.
• Learning to load various datasets and vectors.
• Including personal datasets to the library of Tensorflow Hub.
• Using various Tensorflow technologies to conquer complexities in deployment.

On completing the course, students will be well on their way to create critical Machine Learning models using Tensorflow. They will be extremely familiar with the drawbacks and how to fix them. They can apply for jobs in quality control and deployment.

Prerequisites: Functional understanding of neural networks and deep learning.

Level: Intermediate

Rating: 4.5

Duration: 4 months

You can sign up here

8. Introduction to Tensorflow

This project takes a hands-on approach to teach students how to use Tensorflow to train their models and breed live results. The course revolves arouMachine Learning's fundamentals and implements its technology to create solutions for real-world problems.

The course covers topics such as:

• Basics of Tensorflow and its different versions.
• Fundamentals of Machine Learning.
• Learning about Tensors and Variables.
• Working with Operations.
• Learning to explore a project.
• Working with training models.
• Learning to use Python in JavaScript.
• Learning to save models and convert them to the web.

On completing the course, students can apply for jobs in Machine Learning and programming with Tensorflow. They can also apply for promotions or career change.

Prerequisites: The website does not mention any prerequisites

Level: Intermediate

Rating: 4.4

Duration: The website does not mention the duration of this course.

You can sign up here

9. Complete Guide to Tensorflow for Deep Learning with Python

This course teaches students to use Google Deep Learning to find solutions to real-world problems. It uses advanced technology to solve issues through the use of Tensorflow with Python.

The course covers topics such as:

• Learning to install TensorFlow and set up the environment.
• Fundamentals of Machine Learning.
• A crash course in Numpy, Pandas, and Data Visualization.
• Learning the basics of Scikit.
• Fundamentals and usage of Neural Networks.
• Learning how to use Perception.
• Learning how to activate Neural Networks.
• Creating Neural Networks from scratch.
• Learning gradient descent in BackPropogation.
• Learning various cost functions.
• Fundamentals of Tensorflow.
• Using Graphs, variables, and placeholders in Tensorflow.
• Learning Regression in Tensorflow.
• Learning how to save and restore models in Tensorflow.
• Learning A to Z of Convolutional Neural Networks.
• Using CNN theory and MNIST Basic Approach.
• Learning and practicing Recurrent Neural Networks.
• Implementing Recurrent Neural Networks in Tensorflow.
• Theory and use of Word2Vec.
• Using AutoEncoders and Generative Adversarial Networks.

Students will have a thorough understanding of Neural Networks to develop cutting-edge programs in Python on completing the course. They can compete for more advanced positions to use Tensorflow for Deep Learning.

Prerequisites: Basic knowledge of Mathematics and Python programming

Level: Intermediate

Rating: 4.4

Duration: 14 hours and 9 minutes

You can sign up here

10. Introduction to Tensorflow

This course is dedicated to teaching students Tensorflow and Keras to build Machine Learning models, train them, and deploy them. These components are flexible to use and define in order to create more accurate designs.

The course covers topics such as:

• Fundamentals of Tensorflow and Keras.
• Basics of Machine Learning.
• Designing and building Machine Learning Models using TensorFlow and Keras.
• Checking the accuracy of Machine Learning Models.
• Coding machine learning models for scaled use.
• Learning the hierarchy of Tensorflow 2.0 and its components.
• Using Tensors, Variables, etc. in practical exercises for a hands-on designing experience.
• Learning to use Numpy arrays.
• Featuring datasets and columns to load and modify coding.
• Using Keras sequential API to create accurate Tensorflow models.
• Learning Predictions to deploy models accordingly.
• Learning the basics of Neural Networks.
• Functionalities of Neural networks and how to incorporate them.
• Defining multiple inputs.
• Working with ad hoc acyclic network graphs.
• Learning regularization to optimize the performance of your models.

On completing the course, students can create their personalized models to meet the technological requirements. They can sell their models for monetary value. They can also apply for jobs in creating Machine Learning Models.

Prerequisites: The website does not mention any prerequisites.

Level: Intermediate

Rating: 4.4

Duration: 19 hours

You can sign up here

Conclusion

Tensorflow is an advanced library that is used to train machine learning models. The ability to perform this task flawlessly and with great accuracy puts students on top of the employment list. It is a simple scenario of supply and demand. There is a huge demand as technology is evolving to solve every problem known to mankind. Artificial Intelligence and Neural networks play a major role in this direction. Therefore, if developers can program these models, their demand grows exponentially every day. This is the perfect time to learn the skill and begin a career in technology's advanced fields.

You may also want to check out ML and AI courses here for a further understanding of TensorFlow. Which course do you prefer and why? Share with the community below in the comments. 

People are also reading:

• How to Install Tensorflow?
• AI Books
• Types of AI
• AI Technologies
• Benefits of Artificial Intelligence
• Future of Artificial Intelligence
• AI Applications
• What is Artificial Intelligence?
• Difference between AI vs Machine Learning
• Data Science Projects
• R For Data Science