tensorflow course

10 Best + Free Online TensorFlow Courses

TensorFlow is a computer framework that uses information flow charts to numerically compute mathematical statements. It was developed by Google specifically for Machine Learning. In fact, it has been widely used to develop Deep Learning-based solutions. TensorFlow is one of the most widely used deep learning frameworks. It’s utilized for anything from cutting-edge learning research to developing new characteristics for Silicon Valley’s most popular start-ups. Since TensorFlow became widely available, the popularity of automating actions has grown.
Add in the strong demand for professional individuals who are familiar with TensorFlow, and you have the perfect setup. All of these factors have resulted in an increase in the number of people interested in learning about TensorFlow. Why should you be left behind, then? However, there is a lot of false information out there as well. As a result, we at Courseholic have prepared a list of the 10 top TensorFlow tutorials and courses to learn about TensorFlow with the help of a team of ten machine learning specialists. So, without spending any more time, let’s get started!

List of 10 Best + Free Online TensorFlow Courses

1. The Complete Guide to TensorFlow for Deep Learning with Python(Udemy)

This course will show you how to design artificial neural networks for deep learning using Google’s TensorFlow framework! This course aims to provide you with an easy-to-understand introduction to the complexity of Google’s TensorFlow framework. Other tutorials and courses have steered clear of pure TensorFlow in favor of abstractions that give the user less control.
Here we give a course that serves as a thorough tutorial to using the TensorFlow framework as intended, while also demonstrating the most cutting-edge deep learning approaches!
With complete jupyter notebook code guides and easy-to-reference slides and notes, this course is meant to blend theory and practical application. Along the way, we’ll have lots of exercises to put your new skills to the test!

What you will learn –

  • Understand how Neural Networks Work.
  • Build your own Neural Network from Scratch with Python.
  • Use TensorFlow for Classification and Regression Tasks.
  • Use TensorFlow for Image Classification with Convolutional Neural Networks.
  • Use TensorFlow for Time Series Analysis with Recurrent Neural Networks.
  • Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders.
  • Learn how to conduct Reinforcement Learning with OpenAI Gym.
  • Create Generative Adversarial Networks with TensorFlow.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 14 hours approx

Instructor – Jose Portilla

2. Tensorflow Deep Learning Certification Course (Coursera)

This specialty will assist you in launching a career in artificial intelligence. You’ll learn the fundamentals of Deep Learning, how to create neural networks, and how to lead successful ML projects with this collection of five courses. There are also chances to work on case studies from a variety of real-world companies. The practical exercises will allow you to put your Python and Tensorflow skills to the test. There are also speeches by top leaders in the area that will motivate you and help you comprehend the circumstances that you will face in this line of work.

What you will learn –

  • Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications.
  • Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow.
  • Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data.
  • Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 20 weeks approx

Instructor – Andrew Ng

3. Machine Learning with TensorFlow + Real-Life Business Case(Udemy)

We frequently hear about artificial intelligence, self-driving cars, and Google, Facebook, and Amazon’s “algorithmic wizardry.” It’s not magic, though; it’s deep learning. Deep neural networks, in particular, are frequently used as the “one algorithm to govern them all.”
Cool, that sounds like a valuable skill; how do I become a Deep Learning Master?
There are two routes you can take:
The unguided path – This route will eventually bring you where you want to go, but be prepared to get lost a few times. If you’re looking at this course, chances are you’ve already been there.
The 365 path – Consider our route to be a tour guide. We’ll take you to all of the sites you need to go, using paths only the most knowledgeable tour guides are aware of. We have extra knowledge that you won’t acquire from reading those information boards, and we provide it to you in pleasant and digestible ways to ensure that it sticks.

What you will learn –

  • Gain a Strong Understanding of TensorFlow – Google’s Cutting-Edge Deep Learning Framework.
  • Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow.
  • Set Yourself Apart with Hands-on Deep and Machine Learning Experience.
  • Grasp the Mathematics Behind Deep Learning Algorithms.
  • Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules.
  • Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization.
  • Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 6 hours approx

Instructor – 360 Careers

4. TensorFlow in Practice Specialization (Coursera)

TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models. In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects.

What you will learn –

  • Best practices for TensorFlow, a popular open-source machine learning framework to train a neural network for computer vision applications.
  • Handle real-world image data and explore strategies to prevent overfitting, including augmentation and dropout.
  • Build natural language processing systems using TensorFlow.
  • Apply RNNs, GRUs, and LSTMs as you train them using text repositories.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 24 hours approx

Instructor – Laurence Moroney  

5. TensorFlow: Getting Started (PluralSight)

It’s challenging to create powerful machine learning solutions. There are numerous processing steps that must be completed, and the manner in which these processes are completed is determined by not only the code you write but also the data you use. By learning TensorFlow from the ground up in this course, TensorFlow: Getting Started, you’ll understand how TensorFlow effortlessly tackles these challenges. 

What you will learn –

  • How to instal the software, create basic and advanced models, and use additional libraries to make development even easier. 
  •  TensorFlow’s unique architecture allows you to run your computations on devices as tiny as a Raspberry Pi and as large as a data farm. 
  • Look at how to use TensorFlow with neural networks in general, as well as strong deep neural networks in particular. 
  • Understanding of TensorFlow and being able to use it to build your own machine learning solutions.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 3 hours approx

Instructor – Jerry Kurata

6. Advanced Machine Learning with TensorFlow (Coursera)

This 5-course specialty focuses on advanced machine learning topics utilizing Google Cloud Platform, and you’ll receive hands-on experience improving, deploying, and scaling several types of production ML models in hands-on labs. This specialty continues where “Machine Learning on GCP” ended, teaching you how to create scalable, accurate, and production-ready models for structured data, picture data, time-series, and natural language text. The program concludes with a course on developing recommendation systems. Because earlier courses introduce topics that are addressed in later courses, it is advised that you study the courses in this order.

What you will learn –

  • Learn how to create models for structured data, picture data, time series, and natural language text that are accessible, precise, and ready for production.
  • Examine the components and best practises of a high-performing machine learning system in a production setting.
  •  Examine various methods for constructing an image classifier with convolutional neural networks while enhancing the model’s accuracy through augmentation and feature extraction.
  •  Learn about sequence models and their applications, including an overview of sequence model topologies and how to manage variable-length inputs.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 12 weeks approx

Instructor – Google Cloud Training

7. Machine Learning with TensorFlow by AWS / Kaggle (Udacity)

If you work in the machine learning area, you’re probably familiar with TensorFlow and how important it is. This nanodegree program will teach you how to use TensorFlow to master the fundamentals of machine learning. You’ll acquire hands-on experience with every method in TensorFlow and scikit-learn, from fundamental concepts like data manipulation through supervised algorithms. The course was developed by Udacity’s specialist educators, who have extensive experience with machine learning and TensorFlow. The teachers will be in direct contact with you via live sessions throughout the program to assist you to understand the hard concepts.

What you will learn –

  • Learn about supervised learning, a common class of methods for model construction.
  • Learn the foundations of neural network design and training in TensorFlow.
  • Learn to implement unsupervised learning methods for different kinds of problem domains.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 12 weeks approx

Instructor – Cezanne Camacho,Mat Leonard,Luis Serrano,Dan Romuald Mbanga

8. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning (Coursera)

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

What you will learn –

  • Learn best practices for using TensorFlow, a popular open-source machine learning framework.
  • Build a basic neural network in TensorFlow.
  • Train a neural network for a computer vision application.
  • Understand how to use convolutions to improve your neural network.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 30 hours approx

Instructor – Laurence Moroney  

9. Deep Learning Certification by IBM (edX)

Through a series of hands-on assignments and projects, you will acquire and excel at Deep Learning abilities during this professional certificate program. The course will conclude in a Deep Learning capstone project that will help you display your applied abilities to potential employers. It will be available on the renowned eLearning site edX. You’ll master the fundamentals of Deep Learning, including multiple Neural Networks for both supervised and unsupervised learning, among other things. You’ll also learn how to create and deploy Convolutional Networks, Recurrent Networks, and Autoencoders, among other Deep Architectures. Joseph Santarcangelo, Ph.D., IBM Data Scientist; Alex Aklson, Ph.D., IBM Data Scientist; and Saeed Aghabozorgi, Ph.D., IBM Sr. Data Scientists are the instructors for this program.

What you will learn –

  • Fundamental concepts of Deep Learning, including various Neural Networks for supervised and unsupervised learning.
  • Build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
  • Application of Deep Learning to real-world scenarios such as object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
  • Master Deep Learning at scale with accelerated hardware and GPUs.
  • Use of popular Deep Learning libraries such as Keras, PyTorch, and Tensorflow applied to industry problems.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 32 weeks approx

Instructor – Romeo Kienzler,Samaya Madhavan,Saeed Aghabozorgi,Joseph Santarcangelo,Alex Aklson

10. Building and Deploying Deep Learning Applications with TensorFlow (Linkedin Learning)

One of the most prominent deep learning frameworks is TensorFlow. It’s utilized for anything from cutting-edge machine learning research to the development of new features for Silicon Valley’s hottest start-ups. Learn how to install TensorFlow and use it to create a simple deep learning model in this lesson. Instructor Adam Geitgey demonstrates how to design and train a machine learning model, as well as how to use visualization tools to examine and enhance your model, after demonstrating how to get TensorFlow up and running. Finally, he discusses how to deploy models on a local or cloud basis. When you finish this course, you’ll be able to use TensorFlow to create and deploy your own models.

What you will learn –

  • Learn how to install TensorFlow and use it to build a simple deep learning model. 
  • How to get TensorFlow up and running, instructor Adam Geitgey demonstrates how to create and train a machine learning model, as well as how to leverage visualization tools to analyze and improve your model.
  • How to deploy models locally or in the cloud. 

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 2 hours approx

Instructor – Adam Geitgey

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