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 answers | TensorFlow trending topics |
| TensorFlow / Deep Learning Engineer | Crack 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 TensorFlow | Hands-on deep and machine learning experience |
| Backpropagation, Stochastic Gradient Descent, Batching, Momentum and Learning Rate Schedules | Pre-Processing, Standardization, Normalization and One-Hot Encoding |
| Deep Learning Algorithms from Scratch in Python | Mathematics 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 exam | Interactive notebooks and course slides as downloadable guides |
| Integrate Machine Learning into tools and applications | Building image recognition, text recognition algorithms |
| Deep Learning for Time Series Forecasting | TensorFlow models using Computer Vision, Convolutional Neural Networks and Natural Language Processing |
| Building Machine Learning Models using TensorFlow 2 | Real-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/CNNs | Time Series Forecasting |
| Building Deep Reinforcement Learning Stock Trading Bot | Recommender Systems |
| RESTful API for TensorFlow model serving | TensorFlow Distribution Strategies |
| NLP with Deep Learning | Image classifiers |
| Stock returns | Computer visions |
| GANs | Image Recognition |
| RNNs | Image Recognition |
| TensorFlow Lite | Moore’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 Learning | Image classification using CNNs |
| Forecasting time series data with RNNs | Style transfer using deep learning |
| API for serving TensorFlow models | Building models using Keras API to run on TensorFlow 2 |
| Medical Imaging using deep learning | GANs to generate images |
| Text generation using RNNs and natural language processing | GPUs 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 Fundamentals | TensorFlow and Tensorboard |
| Convolution and its Role in CNNs | Dropout |
| Multiple classification | Image into Tensors |
| Computer Vision and Machine Learning tasks | Kernels |
| L2 regularisation and weight decay | Tensorboard 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 projects | Reusable libraries |
| Evaluating the performance of deep learning models | Training on local machines and Google collaboration |
| Organisation and structure of deep learning projects | Classification projects |
| Loading data in the Numpy array | Dataset 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 2.0 | ANN models |
| Theory and mathematics behind ANNs and CNNs | Back-propagation and gradient descent methods to train ANNs |
| ANNs to perform regression tasks | Trained ANN models for classification tasks |
| Developing ANNs in Google Colab | Models graph and assess their performance in TensorFlow |
| ANN optimisation for hyperparameters | Trained ANN models for regression tasks using KPI |
| Convolutional Neural Networks to classify images | 10 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 basics | TensorFlow and neural networks basics |
| Advanced TensorFlow models | Classification Model Evaluation |
| Overfitting and Underfitting | Advanced augmentation strategies |
| Machine Learning Operations | Deploying API to the Cloud |
| Machine Learning Operations | Quantization Aware training |
| Linear Regression, Logistic Regression and Neural Networks | CNNs for malaria detection |
| Evaluating Classification Models | Data augmentation with TensorFlow, Albumentations with TensorFlow 2 and PyTorch |
| Eager and Graph Modes | Tracking, dataset versioning with Wandb |
| Human emotions detection | Grad-cam method |
| TensorFlow to Onnx Model | Fastapi 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 Science | ANNs, RNNSs and CNNs in TensforFlow 2.0 |
| Ba stock market trading bot using Reinforcement Learning | Data Validation and Dataset Preprocessing |
| Fashion API with Flask and TensorFlow 2.0 | Difference between Tensorflow 1.x and Tensorflow 2.0 |
| Transfer Learning application in Tensorflow 2.0 | Machine Learning Pipeline in Tensorflow 2.0 |
| TensorFlow 2.0 model into production | Serving TensorFlow model with RESTful API |
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Do I need experience to learn Advanced TensorFlow models?
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.
Is it mandatory to learn the theory and mathematics behind ANNs and CNNs for a job?
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.
Is it tough to learn Dataset designing from collection to HDF5 partitioned dataset?
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.
Can I use TensorFlow models in Computer Vision?
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.
Do I need coding experience to learn TensorFlow?
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.
What are some trending topics of TensorFlow?
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.