Are you looking for the Best Keras Online Courses?… If yes, then this article is for you. In this article, you will find the Best Keras Online Courses for beginners & advanced like Beginner courses, and Practice test courses. So, check these Best Keras Online Courses and find the best keras Online Courses according to your need.
Introduction to Deep Learning and Neural networks with Keras – Coursera
Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.
After completing this course, you will be able to:
- Describe what a neural network is, what a deep learning model is, and the difference between them.
- Demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines.
- Demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks.
- Build deep learning models and networks using the Keras library.
From this Introduction to Deep Learning & Neural Networks with kears you will learn topics like
- Introduction to Neural networks & Deep Learning
- Artificial Neural Networks
- Keras & Deep Learning Libraries
- Deep Learning Models
- Course Project
Course Duration: 8 hrs | Best for: Intermediate Level | Provider: IBM
Pricing: free audit course, certification (Paid)
Info: Visit this Keras course
Deep Learning – Udacity
This Nano-Degree program from Udacity will give you a complete understanding of Deep Learning. In this program, you will build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation. You will build projects in Keras and NumPy, in addition to TensorFlow PyTorch.
Now, let’s see the topics covered in that program-
Topics Covered-
- Introduction to Deep Learning.
- Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Generative Adversarial Networks
- Deploying a Sentiment Analysis Model
Extra Benefits-
- You will get a chance to work on Real-world projects.
- You will get Technical mentor support.
- Along with that, you will get Career Services.
Info: Visit this Course
Complete TensorFlow 2 & Keras Deep Learning Bootcamp – Udemy
Rating: 4.7
This course will guide you through how to use Google’s latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google’s TensorFlow 2 framework in a way that is easy to understand.
on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0’s official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more!
This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes.
What you will learn from this course:
- Learn to use TensorFlow 2.0 for Deep Learning
- Perform Image Classification with Convolutional Neural Networks
- Forecast Time Series data with Recurrent Neural Networks
- Use deep learning for style transfer
- Serve Tensorflow Models through an API
- Leverage the Keras API to quickly build models that run on Tensorflow 2
- Use Deep Learning for medical imaging
- Use Generative Adversarial Networks (GANs) to generate images
- Generate text with RNNs and Natural Language Processing
- Use GPUs for accelerated deep learning
This course covers a variety of topics, including
- NumPy Crash Course
- Pandas Data Analysis Crash Course
- Data Visualization Crash Course
- Neural Network Basics
- TensorFlow Basics
- Keras Syntax Basics
- Artificial Neural Networks
- Densely Connected Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- AutoEncoders
- GANs – Generative Adversarial Networks
- Deploying TensorFlow into Production
- and much more!
Course Duration: 19hrs | Articles: 2 | Downloadable resources: 3
Info: Visit this course and get amazing offers before the flash sale ends
Applied AI with Deep Learning – Coursera
This Advanced Level Applied AI with Deep Learning course will introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs.
Skills required to learn this course:
Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra).
In this Applied AI with Deep Learning course will teach you the topics like:
- Introduction to Deep learning
- Deep Learning Frameworks
- Deep Learning Applications
- Scaling and Deployment
Best for: Advanced Level | Course Duration: 25hrs | Provider: IBM
Pricing: Free Audit course, if certification needed(paid)
Info: Visit this Course and get offers on this course
TensorFlow 2 for Deep Learning Specialization – Coursera
This Specialization is intended for machine learning researchers and practitioners who are seeking to develop practical skills in the popular deep learning framework TensorFlow.
The first course of this Specialization will guide you through the fundamental concepts required to successfully build, train, evaluate and make predictions from deep learning models, validating your models and including regularisation, implementing callbacks, and saving and loading models.
The second course will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. You will also expand your knowledge of the TensorFlow APIs to include sequence models.
The final course specialises in the increasingly important probabilistic approach to deep learning. You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning. As such, this course can also be viewed as an introduction to the TensorFlow Probability library.
Skills you will gain from this Course:
- Tensorflow
- keras
- TensorFlow Probability
- Probabilistic Neural Networks
- Deep Learning
- Probabilistic Neural Network
- Probabilistic Programming Language (PRPL)
In this Specialization course you will learn 3 concepts. They are:
- Getting Started with TensorFlow2
- Customizing your Models with TensorFlow2
- Probabilistic deep Learning with TensorFlow 2
Best for: Intermediate Level | Course Duration: 4 months | Provider: Imperial College London
Info: Visit this Course and get amazing offers on this Course
And here the list ends. So, these are the Best Keras Online Courses. I will keep adding more Best Keras Online Courses to this list.
Conclusion
I hope these Best Keras Online Courses will definitely help you to enhance your skills. If you have any doubts or questions, feel free to ask me in the comment section.