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TensorFlow Courses

TensorFlow is an open-source Machine Learning library developed by Google which is mainly used for numerical computation and to train Deep Learning models using several APIs and tools. Udemy features more than 1,000 courses on TensorFlow.

The article features the 10 best Udemy TensorFlow Courses in 2023. As per student ratings and reviews the ‘TensorFlow Developer Certificate in 2023: Zero to Mastery Course’ is one of the best TensorFlow courses on Udemy. More than 59,000 students have enrolled in the course. It has an average rating of 4.7/5 based on over 6,000 reviews. The ‘Deep Learning with TensorFlow 2.0 [2023] Course’ is another popular Udemy TensorFlow course. It has an average student rating of 4.5/5 based on over 2,200 reviews. 

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1. TensorFlow Interview Questions & Answers

The TensorFlow Interview Questions & Answers course covers the top questions and answers asked in TensorFlow job interviews. Students will get to know the latest and most frequently asked questions in this course to prepare them for roles like Deep Learning or ML engineers.

  • Course Rating: 4.9/5
  • Duration: 3 hours 7 minutes
  • Benefits: Certificate of completion, Mobile and TV access, Lifetime access, 1 downloadable resource

Learning Outcomes

TensorFlow interview questions and answersTensorFlow trending topics
TensorFlow / Deep Learning EngineerCrack TensorFlow and Deep Learning / Machine Learning job interviews

2. Deep Learning with TensorFlow 2.0 [2023]

The Deep Learning with TensorFlow 2.0 [2023] course teaches students about TensorFlow 2.0 and how it can be used to write deep learning algorithms. Students will get hands-on training in real-world cases to optimise businesses using deep learning. The theories and mathematics behind different algorithms are also explained in detail. Topics covered include TensorFlow and NumPy, layers, backpropagation process, initialization methods and more. Students will have a strong knowledge of neural networks and will be able to apply skills in business cases after completing the course.

  • Course Rating: 4.5/5
  • Duration: 5 hours 55 minutes
  • Benefits: Certificate of completion, Mobile and TV access, 18 articles, 20 downloadable resources

Learning Outcomes

Strong understanding of TensorFlowHands-on deep and machine learning experience
Backpropagation, Stochastic Gradient Descent, Batching, Momentum and Learning Rate SchedulesPre-Processing, Standardization, Normalization and One-Hot Encoding
Deep Learning Algorithms from Scratch in PythonMathematics Behind Deep Learning Algorithms
Underfitting, Overfitting, Training, Validation, Testing, Early Stopping and Initialization

3. TensorFlow Developer Certificate in 2023: Zero to Mastery

The TensorFlow Developer Certificate in 2023: Zero to Mastery course aims to impart all the necessary skills required so that students can clear the TensorFlow Developer Certification exam by Google. Different topics are covered in 10 chapters and a milestone project. Students will have the skills to develop modern deep-learning solutions after completing this course.

  • Course Rating: 4.7/5
  • Duration: 63 hours 32 minutes
  • Benefits: Certificate of completion, Mobile and TV access, Lifetime access, 42 articles, 5 downloadable resources, 1 coding exercise

Learning Outcomes

Skills to pass the Google TensorFlow Developer Certificate examInteractive notebooks and course slides as downloadable guides
Integrate Machine Learning into tools and applicationsBuilding image recognition, text recognition algorithms
Deep Learning for Time Series ForecastingTensorFlow models using Computer Vision, Convolutional Neural Networks and Natural Language Processing
Building Machine Learning Models using TensorFlow 2Real-world images to visualise the journey of an image

4. TensorFlow 2.0: Deep Learning and Artificial Intelligence

The TensorFlow 2.0: Deep Learning and Artificial Intelligence course is beginner-friendly and aims to teach students the fundamentals of TensorFlow. Students will learn about basic machine learning models and will be going up to modern concepts in a step-by-step method. Students will also learn about deep learning architectures and test their skills in featured projects. Upon completion of the course, students will gain knowledge in machine learning, neural networks and many more TensorFlow and deep learning topics.

  • Course Rating: 4.7/5
  • Duration: 23 hours 27 minutes
  • Benefits: Certificate of completion, Mobile and TV access, Lifetime access

Learning Outcomes

ANNs/DNNs/CNNsTime Series Forecasting
Building Deep Reinforcement Learning Stock Trading BotRecommender Systems
RESTful API for TensorFlow model servingTensorFlow Distribution Strategies
NLP with Deep LearningImage classifiers
Stock returnsComputer visions
GANsImage Recognition
RNNsImage Recognition
TensorFlow LiteMoore’s Law using code

5. Complete TensorFlow 2 and Keras Deep Learning Bootcamp

The Complete TensorFlow 2 and Keras Deep Learning Bootcamp Course will help students learn to create artificial neural networks for deep learning using TensorFlow 2 in this course. Students will understand the complexities of TensorFlow 2 and learn to use Keras API to build models. The course also features Jupyter guides and references for students.

  • Course Rating: 4.6/5
  • Duration: 19 hours 12 minutes
  • Benefits: Certificate of completion, Mobile and TV access, 2 articles, 3 downloadable resources

Learning Outcomes

TensorFlow 2.0 for Deep LearningImage classification using CNNs
Forecasting time series data with RNNsStyle transfer using deep learning
API for serving TensorFlow modelsBuilding models using Keras API to run on TensorFlow 2
Medical Imaging using deep learningGANs to generate images
Text generation using RNNs and natural language processingGPUs for better deep learning

6. Convolutional Neural Networks with TensorFlow in Python

The  Convolutional Neural Networks with TensorFlow in Python course deals with advanced neural networks mainly CNNs and deep learning. Students will learn about CNN basics and techniques to improve the performance of models and review popular CNN architectures. Upon completion of the course, students will have the skills to work on CNN projects confidently. Basic knowledge of TensorFlow and deep learning concepts is necessary before starting this course.

  • Course Rating: 4.7/5
  • Duration: 4 hours 42 minutes
  • Benefits: Certificate of completion, Mobile and TV access

Learning Outcomes

Convolutional Neural Network FundamentalsTensorFlow and Tensorboard
Convolution and its Role in CNNsDropout
Multiple classificationImage into Tensors
Computer Vision and Machine Learning tasksKernels
L2 regularisation and weight decayTensorboard to visualise networks and metrics
Concepts behind CNN architectures

7. Deep Learning in Practice I: Tensorflow Basics and Datasets

The  Deep Learning in Practice I: Tensorflow Basics and Datasets course deals with providing students with skills and best practices which will help them in deep learning projects. Students will learn about the structuring of projects efficiently so that they can be reused and deep learning project processes from data collection to evaluation. Students will be able to develop deep learning projects and dataset design efficiently with good results after completing this course.

  • Course Rating: 4.2/5
  • Duration: 4 hours 26 minutes
  • Benefits: Certificate of completion, Mobile and TV access, Lifetime access, 6 articles, 5 downloadable resources, assignments

Learning Outcomes

Complex deep-learning projectsReusable libraries
Evaluating the performance of deep learning modelsTraining on local machines and Google collaboration
Organisation and structure of deep learning projectsClassification projects
Loading data in the Numpy arrayDataset designing from collection to HDF5 partitioned dataset

8. TensorFlow 2.0 Practical

The TensorFlow 2.0 Practical course aims at teaching TensorFlow 2.0 to students through 10 different projects. Students will get hands-on experience in ANNs and CNNs using TensorFlow and Google Colab with real data. Topics covered include ANNs for regression tasks, model visualisations, sentiment analysis, TensorFlow 2.0 for task deployment and more. Students will gain knowledge in using TensorFlow 2.0 models for real-world problem-solving in this course.

  • Course Rating: 4.7/5
  • Duration: 11 hours 44 minutes
  • Benefits: Certificate of completion, Mobile and TV access, 2 articles, 10 downloadable resources

Learning Outcomes

TensorFlow 2.0ANN models
Theory and mathematics behind ANNs and CNNsBack-propagation and gradient descent methods to train ANNs
ANNs to perform regression tasksTrained ANN models for classification tasks
Developing ANNs in Google ColabModels graph and assess their performance in TensorFlow
ANN optimisation for hyperparametersTrained ANN models for regression tasks using KPI
Convolutional Neural Networks to classify images10 Projects

9. Deep Learning: Image Classification with Tensorflow in 2023

The  Deep Learning: Image Classification with Tensorflow in 2023 course introduces students to different concepts of deep learning in TensorFlow 2 and Huggingface. Students will learn different concepts in an easy-to-understand step-by-step approach. Students will understand the working of image classification algorithm and their deployment to the cloud via best available practices. Different topics related to deep learning, MLOps, advanced TensorFlow concepts and more are covered in this course.

  • Course Rating: 4.6/5
  • Duration: 32 hours 28 minutes
  • Benefits: Certificate of completion, Mobile and TV access, Lifetime access, 1 article

Learning Outcomes

Tensors and Variables basicsTensorFlow and neural networks basics
Advanced TensorFlow modelsClassification Model Evaluation
Overfitting and UnderfittingAdvanced augmentation strategies
Machine Learning OperationsDeploying API to the Cloud
Machine Learning OperationsQuantization Aware training
Linear Regression, Logistic Regression and Neural NetworksCNNs for malaria detection
Evaluating Classification ModelsData augmentation with TensorFlow, Albumentations with TensorFlow 2 and PyTorch
Eager and Graph ModesTracking, dataset versioning with Wandb
Human emotions detectionGrad-cam method
TensorFlow to Onnx ModelFastapi to build API

10. A Complete Guide on TensorFlow 2.0 using Keras API

The  A Complete Guide on TensorFlow 2.0 using Keras API Course will help students learn to make applications based on deep learning and AI using TensorFlow 2 in this course. The course covers all the topics of neural networks in a structured way. The course is divided into 5 parts to make students easily start from the basics and build up to advanced topics as the course proceeds.

  • Course Rating: 4.6/5
  • Duration: 13 hours 5 minutes
  • Benefits: Certificate of completion, Mobile and TV access, 13 articles, 3 downloadable resources

Learning Outcomes

TensorFlow 2.0 in Data ScienceANNs, RNNSs and CNNs in TensforFlow 2.0
Ba stock market trading bot using Reinforcement LearningData Validation and Dataset Preprocessing
Fashion API with Flask and TensorFlow 2.0Difference between Tensorflow 1.x and Tensorflow 2.0
Transfer Learning application in Tensorflow 2.0Machine Learning Pipeline in Tensorflow 2.0
TensorFlow 2.0 model into productionServing TensorFlow model with RESTful API

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By Nikita Joshi

A creative advocate of multi-disciplinary learning ideology, Nikita believes that anything can be learned given proper interest and efforts. She completed her formal education in BSc Microbiology from the University of Delhi. Now proficiently dealing with content ideation and strategy, she's been a part of Coursevise since August 2023 working as a content writer Having worked with several other things during these two years, her primary fields of focus have been SEO, Google Analytics, Website Traffic, Copywriting, and PR Writing. Apart from all that work, Nikita likes to doodle and pen down her rhymes when she feels free.

5 /5
Based on 6 ratings

Reviewed by 6 users

    • 1 month ago

    What are some trending topics of TensorFlow?

      • 1 month ago

      A few of the trending topics are –

      Natural Language Processing (NLP): Techniques for understanding and generating human language.
      Generative Adversarial Networks (GANs): Models that generate new data samples.
      Reinforcement Learning: Algorithms that learn from interactions with an environment.
      Time Series Forecasting: Predicting future values based on historical data.
      MLOps: Machine learning operations, including continuous integration, continuous delivery, continuous training, and continuous production monitoring.

    • 1 month ago

    Do I need coding experience to learn TensorFlow?

      • 1 month ago

      No, you don’t need coding experience to learn TensorFlow. However, having some familiarity with programming concepts (particularly in Python) can help you understand its functionalities. Additionally, with Udemy tutorials, documentation, and resources you can gradually build your coding skills while learning TensorFlow.

    • 1 month ago

    Can I use TensorFlow models in Computer Vision?

      • 1 month ago

      Yes, TensorFlow has powerful tools and frameworks to develop computer vision models. You can use TensorFlow’s high-level APIs like Keras and TensorFlow.js, to build and deploy convolutional neural networks (CNNs) and other deep learning models. Also, you can use it for tasks such as image classification, object detection, image segmentation, and more.

    • 1 month ago

    Is it tough to learn Dataset designing from collection to HDF5 partitioned dataset?

      • 1 month ago

      Learning dataset designing from collection to HDF5 partitioned dataset can be challenging. This is because designing datasets involves understanding data structures, storage formats, and optimization techniques. In this regard, concepts like collections and HDF5 partitioning may be challenging for a beginner. Hence, students must have a systematic approach and can consider online courses for the same.

    • 1 month ago

    Is it mandatory to learn the theory and mathematics behind ANNs and CNNs for a job?

      • 1 month ago

      It is not mandatory to have an in-depth understanding of the theory and mathematics behind ANNs and CNNs for a job. However, in machine learning and deep learning, having some level of knowledge can be helpful. For example, understanding the principles can help you to interpret model behaviour, troubleshoot issues, and make decisions to design, train, and optimize neural network architectures. Also, it can help in roles that involve research, model development, or advanced problem-solving.

    • 1 month ago

    Do I need experience to learn Advanced TensorFlow models?

      • 1 month ago

      Experience in machine learning and TensorFlow can be helpful, however, it’s not necessary. Advanced TensorFlow concepts build upon foundational machine learning principles and TensorFlow basics, so having an understanding of these fundamentals is beneficial.

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