11 Best+Free TensorFlow Tutorial, Course, Training & Certification Online [Updated June 2023]

 

Offered by Deeplearning.ai, the focus of this tensorflow online learning course lies on using the tools software developers use to build AI-powered algorithms that can be scaled. Needless to say, they use TensorFlow to do the same. This is a 4-course tensorflow training specialization where initially you would be taught how to build and train neural networks. Further down the road, you would  try and improve a network’s performance by making use of convolutions by training it to identify real-world images. Via the natural language processing systems, you will be teaching machines to understand, analyze human speech, and respond to it, process text, represent sentences, and input data into a neural network. Finally, you’ll  be getting to train an AI to create original poetry.

 

The following courses make up this Coursera specialization:

• Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
• Convolutional Neural Networks in TensorFlow
• Natural Language Processing in TensorFlow
• Sequences, Time Series and Prediction

 

The key concepts that you will study in this tensorflow course are:

• Enhancing Vision with Convolutional Neural Networks
• Using Real-world Images
• Augmentation: A technique to avoid overfitting
• Multiclass Classifications
• Word Embeddings
• Sequence models and literature
• Deep Neural Networks for Time Series
• Recurrent Neural Networks for Time Series

 

This is an intermediate level tensorflow certification specialization that should not take you more than 1-month to complete at the suggested pace of 14-hours per week.

 

 

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If you do not want to go through the complete tensorflow online learning specialization above and want to skip on the deep exploration of neural networks and natural language processing then you can just choose to study this tensorflow training course. This tensorflow online course  focuses on teaching you the best practices for Tensorflow. Its focus lies on teaching the most important and foundation level principles of Machine Learning and Deep Learning. You will be using TensorFlow to implement those principles and build scalable models and apply them to real-world problems. However, to develop a deeper understanding about neural networks, we recommend that you should take the full tensorflow online training specialization.

 

The major topics covered in this tensorflow certification course are:

• Programming Paradigm
• Computer Vision
• Enhancing Vision with Convolutional Neural Networks
• Using Real-world Images
• Building a basic neural network
• Training a neural network

 

This is an intermediate level course that requires you to have prior experience in Python coding. Also, familiarity with some high school-level math is required. If you have some  machine learning or deep learning knowledge beforehand then it might prove to be helpful but is not absolutely required.

 

 

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This is another tensorflow training specialization that has been offered by deeplearning.ai where you will be taught how to break into AI. This is a 5-course tensorflow course specialization where from the foundations of Deep Learning, building neural networks, to leading successful machine learning projects, you will be taught everything from scratch. Also, convolutional networks, RNNs, and LSTM, along with Adam, Dropout, BatchNorm, and Xavier/He initialization are some of the other focus areas of this specialization. Further down the road you would be dealing with real life case studies from the fields of healthcare,  sign language reading, natural language processing etc. and learn through a perfect blend of both theory and practical in this tensorflow online course.

 

The following 5 courses make up this Coursera tensorflow training specialization:

• Neural Networks and Deep Learning
• Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
• Structuring Machine Learning Projects
• Convolutional Neural Networks
• Sequence Models

 

The major topics covered in the tensorflow online learning course are:

• Neural Networks Basics
• Deep Neural Networks
• Hyperparameter tuning, Batch Normalization and Programming Frameworks
• Practical aspects of Deep Learning
• Machine Learning Strategy
• Deep convolutional models
• Face recognition & Neural style transfer
• Natural Language Processing & Word Embeddings
• Sequence models & Attention mechanism
• Recurrent Neural Networks

 

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Offered by Google Cloud, this tensorflow training specialization will teach you how to convert candidate use case into a machine learning driven application model by going through five phases. Also, through gradient descents, you’d be finding generalized solutions to some of the most common supervised learning problems. When you reach the meaty part of TensorFlow, you will be writing distributed machine learning models, and scale out the training of those models. A bit of the focus would also lie on offering high-performance predictions in this tensorflow online training. By converting raw data into features that make it easy for machines to learn important characteristics from the data, we will try to emulate human insight into any problem.

 

The following courses are a part of this tensorflow course specialization:

• How Google does Machine Learning
• Launching into Machine Learning
• Intro to TensorFlow
• Feature Engineering
• Art and Science of Machine Learning

 

Primary topics covered in the above tensorflow course modules are:

• How Google does ML
• Python notebooks in the cloud
• Generalization and Sampling
• Optimization
• Estimator API
• Scaling TensorFlow models
• Raw Data to Features
• Preprocessing and Feature Creation
• Hyperparameter Tuning
• Embeddings

 

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The focus of this tensorflow training course lies on using TensorFlow with Python to solve real world problems. This 14-hour tensorflow online learning course with 5 downloadable resources is where you will learn how to create artificial neural networks for deep learning through Google’s TensorFlow framework. The complexities of Google’s TensorFlow framework will be presented in an easy to understand way in the form of a guide. Unlike other tensorflow training courses which tend to stay away from TensorFlow and use abstractions instead, this one deals with it head on. The major reason behind doing so is because the tensorflow online training course creators realize that the biggest disadvantage of using abstractions is significantly less user control.

 

The core concepts that you will learn via this tensorflow course are:

• Classification and Regression Tasks
• Time Series Analysis with Recurrent Neural Networks
• Reinforcement Learning with OpenAI Gym
• Neural Network from Scratch with Python
• Convolutional Neural Networks
• Unsupervised Learning Problems with AutoEncoders

 

This is an intermediate level tensorflow tutorial which requires you to have some Python programming knowledge along with some basic math concepts like mean, standard deviation, etc. Through jupyter notebook guides of code, along with some easy to reference slides and notes, this course maintains a perfect balance of theory and practical and has some practice tests to assess your skills throughout the tensorflow tutorial.

 

 

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This tensorflow training, which is offered by IBM, course follows the approach of solving real world problems by making its students learn about TensorFlow. Since most of the world’s data is unstructured, therefore, the focus is mostly on how Deep Learning with TensorFlow can be applied to solve these types of problems. This tensorflow course which is a part of a Deep Learning Professional Certificate, starts off with the basic concepts of TensorFlow, its main functions, its operations and the execution pipeline. Starting off right off the bat with a simple “Hello World” program, you will learn about how can you apply TensorFlow in curve fitting, regression, classification and minimization of error functions.

 

The primary topics explored in this tensorflow course are:

• Foundational TensorFlow concepts
• Convolutional Networks, Recurrent Networks and Autoencoders
• TensorFlow for backpropagation
• Restricted Boltzmann Machine
• Deep Belief Network
• Collaborative Filtering with RBM

 

This is an intermediate level tensorflow online learning course which requires you to have some background knowledge of Python & Jupyter notebooks and Machine Learning, Deep Learning concepts. At the suggested pace of 2–4 hours per week, the tensorflow tutorial will take you around 5-weeks to complete. As always, this EdX tensorflow online course course too is free to take and would only require a payment to obtain a professional certification.

 

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If LinkedIn is the platform of your choice then there are five main tensorflow training courses available to carry on with your learning. Please not that each tensorflow course covers a few different aspects related to TensorFlow and Deep Learning and there is no one tensorflow tutorial that fits all. Therefore our recommendation is to take those courses that cover your area of interest.

 

1. Building and Deploying Deep Learning Applications with TensorFlowThis 1-hour 46-minute intermediate level tensorflow online learning course will teach you how to build a simple deep learning model. Further, you would be leverage visualization tools to analyze and improve your model. And lastly, you would be taught how to deploy models locally or in the cloud.The primary topics covered in this tensorflow online course are:
   • Creating a TensorFlow Model
   • Training a model in TensorFlow
   • TensorBoard
   • Using a trained TensorFlow

    

2. Accelerating TensorFlow with the Google Machine Learning EngineThis is a 3-hour 5-minute intermediate level tensorflow online course course where you would try and build high-performing machine learning applications by leveraging TensorFlow. Not only you would be exploring the process of developing TensorFlow applications but also running them on the Google Cloud Machine Learning (ML) Engine. Starting off with the basic graphs, sessions, variables, and training, you would also cover datasets, iterators, and estimators which are some high-level features.The key concepts covered in this tensorflow tutorial are:
   • Fundamentals of TensorFlow Development
   • Training TensorFlow Applications
   • Accessing Data With Datasets
   • Machine Learning With Estimators

    

3. Learning TensorFlow with JavaScriptThis is another intermediate level tensorflow tutorial which requires around 57-minutes to complete. The course will present to you ML basics, and you will learn how to set up, use TensorFlow. You would be doing this to train a model and generate live results. Further down the road, you would be working with the different tensor types, variables, models, and layers.The major concepts covered are:
   • TensorFlow Basics
   • Exploration Of A Full Project
   • Using Python based model in JS
   • Initial project creation with TensorFlow

    

4. Building Recommender Systems with Machine Learning and AIThis is a comprehensive 9-hour 4-minute tensorflow tutorial for both beginner and intermediate level students that will help you build your own recommender systems. You would be covering recommendation algorithms that are based on neighborhood-based collaborative filtering and also complex topics like matrix factorization.The primary topics covered are:
   • Recommender Engine Framework
   • Content Based Filtering
   • Deep Learning
   • Hybrid Recommenders

    

5. Building Deep Learning Applications with Keras 2.0This intermediate level tensorflow tutorial takes around 1-hour and 24-minutes to complete and covers the popular Keras programming framework. It will be using TensorFlow to provide various functionalities. Finally, you would be learning how to build a simple deep learning model and use pre-trained deep learning models included in Keras.The key concepts in this course are:
   • Setting Up Keras
   • Creating A Neural Network In Keras
   • Monitoring a Keras Model With TensorBoard
   • Trained Keras Model In Google Cloud

    

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Offered by TensorFlow, this tensorflow training course follows a practical approach to Deep Learning. This course is a part of the Become a Machine Learning Engineer NanoDegree Program. It is an intermediate level tensorflow course which requires around 2-months to complete and is completely free to take. The focus of this tensorflow online learning course lies on teaching you how to build deep learning applications with TensorFlow. It is a great resource for some hands-on experience for state-of-the-art image classifiers. You would be using TensorFlow models in various platforms like mobile devices, in the cloud, and in browsers.

 

The major topics covered in this tensorflow certification course are:

• Introduction to Machine Learning
• Introduction to Convolutional Neural Networks ("CNNs")
• Transfer Learning
• Saving and Loading Models
• Introduction to TensorFlow Lite
• Time Series Forecasting

 

The course requires you to have a strong command over beginning Python syntax, and its variables, functions, classes, and object-oriented programming, along with some basic algebra to get the most out of this tensorflow tutorial.

 

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This 6-hour tensorflow training course with 20 downloadable resources teaches you how you can build deep learning algorithms with TensorFlow 2.0, learn in-depth about neural network and apply our learning to a real world business case. The tensorflow certification course requires you to have some basic Python programming skills to understand the content. It follows a business focused approach to give you real world practice about using Deep Learning to optimize business performance. Right from the mathematics behind the algorithms which make up the theory to discussing and solving real world problems, this tensorflow online learning course helps you understand what you are doing and why you are doing it rather than just diving into the content with closed eyes.

 

The key concepts discovered in this tensorflow online course are:

• Learning Rate Schedules
• Normalization, and One-Hot Encoding
• Deep Learning Algorithms from Scratch
• Testing, Early Stopping, and Initialization
• Underfitting And Overfitting - Classification
• Adaptive Moment Estimation

 

You would be building your own algorithms and the certificate of completion at the end of the course will be an impressive addition to your resume. The course is a complete step-by-step blueprint or a hands on guide to build your first Deep Learning algorithm from scratch.

 

 

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This is a medium length 7-hour tensorflow course that will help you make a credit card fraud detection model along with an app to predict the changes in the stock market. Not only will you learn how to code in Python but also, calculate linear regression with TensorFlow. The tensorflow course starts off by making you comfortable with the interface which is what a lot of other courses skip. Needless to say, this results in complicating things for the learners. The tensorflow online course also has a bonus section in the form of a webinar called ‘How To Master Anything’. Though it requires you to have familiarity with university-level math along with some prior coding experience.

 

The major topics covered in the tensorflow online training course are:

• Multivalue Variables
• Control Flow
• Constant and Operation Nodes
• Building Linear Regression
• Building Functions to Connect Graph
• Training a Model

 

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For those looking to learn deep learning and neural networks in tensorflow from scratch, this tensorflow course for beginners is a great resource. This relatively short 3-hour tensorflow tutorial packs in a punch and assumes you to be a complete beginner. This means that each instruction is step-by-step without skipping or skimming over anything by assuming you should already know it. It should help you build your first neural network from scratch. The tensorflow online course does not dive into the math behind things and by the end of it you would’ve created, trained and tested a complete neural network on your own.

 

The primary topics covered in this tensorflow tutorial are:

• Customizing your own neural network
• Basic syntax in tensorflow - operations
• Coding the network
• How to use Transfer learning
• Structure of a neural network
• Error correction

 

The tensorflow online training course requires you to have some basic Python and math knowledge to get the most out of the course material.

 

 

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