What is the best way to learn Keras?

We think the best way to learn Keras is with courses or books. One of the best Keras courses we could find is A Beginner's Guide to Deep Learning on Educative.io. And one of the best books we found is Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow.

Is there a DataCamp Keras course?

DataCamp has a Keras course titled Introduction to Deep Learning with Keras. You'll learn about regression , classification and neural networks using real-world exercises such as predicting asteroid trajectories and reconstructing noisy images.

8 Best Ways to Learn Keras ASAP in 2024 [Keras Books & Courses]

Table of Contents

What is Keras?

Keras is a high-level neural network Python library that acts as an interface for the TensorFlow library. It’s used for developing and evaluating deep learning models.

💡 TensorFlow is an open-source library for machine learning. One of its main tasks is inference and training of neural networks.

How can I learn Keras?

There are plenty of avenues to learn Keras. But we think the most effective are courses and books.

And today we’re showing you the best courses and books to learn Keras.

This post contains affiliate links. I may receive compensation if you buy something. Read my disclosure for more details.

TLDR: Best Ways to Learn Keras

🔥 Best Course 🔥
A Beginner’s Guide to Deep Learning: Educative.io

💥 Best Book 💥
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Learn Keras: Courses

1. A Beginner’s Guide to Deep Learning: Educative.io

Is Educative worth it? Read our review to find out why this platform gives us hearts for eyes. 😍

A Beginner’s Guide to Deep Learning is for students who have experience with Python. Learning deep learning fundamentals, you’ll learn to code both simple and complex deep learning models in Keras and NumPy. By the end of the course, you should be proficient in deep learning basics.

💡 NumPy is a Python library used for scientific computing. It supports multi-dimensional arrays and matrices as well as high-level math functions.

best deep learning courses Educative syllabus and diagrams in lesson — Deep learning explanation in the course **A Beginner’s Guide to Deep Learning** on **Educative.io**

Educative.io has an interactive learning environment where you’ll do all work within the same browser. There’s no downloading anything, so you can get to learning Keras right away.

Keras workflow diagram in the course **A Beginner’s Guide to Deep Learning** on **Educative.io**

🔥 Geena’s Hot Take

This course is $69 per year. But for $199 per year you’ll get access to ALL of Educative.io’s courses on deep learning, machine learning and data science.

This is a no-brainer if you’re serious about getting into the field.

Invest in yourself! 🤑

💰 Cost: $69 per year for the course // $59 per month or $199 per year ($16.66/mo) for all courses and Learning Paths
⏲️ Duration: 20 hours
📉 Level: Beginner
🖥️ Format: Interactive learning
🎖️ Certificate: Yes

2. Deep Learning Tutorial For Beginners: Image Classification with Keras: BitDegree

❓ Want to know what we think of BitDegree? Read our full BitDegree review.

Instructor of the course **Deep Learning Tutorial for Beginners** on **BitDegree**

Deep Learning Tutorial for Beginners is a bootcamp-style introduction to Keras, Python and PyCharm. In addition to understanding machine learning neural networks, you’ll:

format datasets
build the image classifier model
save and load trained models

And much more.

💡 PyCharm is an integrated developer environment (IDE) for Python programming.

What you’ll learn in the course **Deep Learning Tutorial for Beginners** on **BitDegree**

This BitDegree course is video-based. You’ll do all work on your local machine. If you like Udemy-style courses where you Netflix and chill, this course is for you.

Fun fact! BitDegree is the first blockchain-powered education platform. This means you can earn tokens (BDG) and use them for more courses or convert them into Ethereum.

💰 Cost: $48.32
⏲️ Duration: 8.75 hours
📉 Level: Beginner
🖥️ Format: Video
🎖️ Certificate: Yes

3. Introduction to Deep Learning with Keras: DataCamp

Is DataCamp worth it? Read our DataCamp review to find out what we love about this platform. 💜

Building a simple neural network with Keras in the course **Introduction to Deep Learning with Keras** on **DataCamp**

If you’re pursuing machine learning as a career, then DataCamp is a must. Introduction to Deep Learning with Keras teaches concepts like regression, classification and neural networks. It uses real-world exercises such as:

predicting asteroid trajectories
distinguishing between real and fake money
reconstructing noisy images

And beyond.

Video on training your model in the course **Introduction to Deep Learning with Keras** on **DataCamp**

DataCamp uses both an interactive learning environment like Educative.io and videos like BitDegree. It’s this magical blend that creates a stimulating learning environment on the DataCamp platform.

💰 Cost: $25 per month for all courses and Learning Paths // $33.25 per month for courses, Learning Paths, projects and support
⏲️ Duration: 4 hours
📉 Level: Beginner
🖥️ Format: Interactive learning & video
🎖️ Certificate: Yes

💡 Ready to take your skills to the next level? Check out the course Advanced Deep Learning with Keras on DataCamp.

4. Introduction to Deep Learning & Neural Networks with Keras: Coursera

Coursera gives us mixed feelings. But there are some good courses (like this one). 🧠

Module 2 graphs in the course **Introduction to Deep Learning & Neural Networks** **with Keras** on **Coursera**

Introduction to Deep Learning & Neural Networks with Keras starts with the basics of neural networks and deep learning. As you progress, you’ll demonstrate your knowledge of supervised and unsupervised deep learning models. Finally using the Keras library, you’ll learn how to build deep learning networks and models.

Neural networks in the course **Introduction to Deep Learning & Neural Networks with Keras** on **Coursera**

Like BitDegree, Coursera is a video-based learning platform where you’ll do all work outside the browser on your local machine. But Coursera has a much more hands-on approach where you’ll work in-depth with the course material.

💰 Cost: $49 per month
⏲️ Duration: 8 hours
📉 Level: Intermediate
🖥️ Format: Video
🎖️ Certificate: Yes

Learn Keras: Books

5. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

↘️ Author: Aurelien Geron
↘️ Publisher: O’Reilly
↘️ Ideal for: programmers with no prior machine learning experience
↘️ Topics covered: Keras, TensorFlow, scikit-learn

hands on machine learning book cover with salamander

In this exercise-heavy book, you’ll start by learning the basics of deep learning like linear regression. Then you’ll graduate to deep neural networks. After that, you’ll explore training models such as support vector machines and random forests. Finally, you’ll discover how to use Keras, TensorFlow and scikit-learn to build intelligent systems.

PICK UP HANDS-ON MACHINE LEARNING WITH SCIKIT-LEARN, KERAS & TENSORFLOW

6. Deep Learning with Python

↘️ Author: François Chollet
↘️ Publisher: Manning
↘️ Ideal for: Python programmers
↘️ Topics covered: Python, Keras

Deep Learning with Python was written by the creator of Keras François Chollet. Using Python programming, you’ll dive into the field of deep learning using the Keras library. With plenty of practice problems, you’ll explore natural language processing (NLP), generative models and computer vision.

💡 Computer vision trains computers to obtain a high-level understanding of images or videos.

GRAB YOUR COPY OF DEEP LEARNING WITH PYTHON

7. Deep Learning with TensorFlow 2 and Keras

↘️ Authors: Antonio Gulli, Amita Kapoor, Sujit Pal
↘️ Publisher: Packt
↘️ Ideal for: Python developers and data scientists
↘️ Topics covered: TensorFlow 2, Keras, building machine learning systems

deep learning with tensorflow 2 and keras book cover

Deep Learning with TensorFlow 2 and Keras teaches you about neural networks and deep learning techniques through the lens of TensorFlow 2 and Keras. Using this scalable machine learning stack, you’ll learn how to write deep learning applications. You’ll also discover convolutional neural networks (ConvNets), generative adversarial networks (GANS) and recurrent neural networks (RNNS).

PICK UP DEEP LEARNING WITH TENSORFLOW 2 AND KERAS

8. Deep Learning Illustrated

↘️ Authors: John Krohn, Grant Beyleveld, Aglae Bassens
↘️ Publisher: Addison-Wesley Professional
↘️ Ideal for: Python programmers, deep learning newbies
↘️ Topics covered: deep learning techniques, TensorFlow & Keras, PyTorch, Coach

Using illustrations and analogies, Deep Learning Illustrated starts by explaining what deep learning is. Then you’ll learn a series of deep learning techniques using limited math and ample Python programming. Finally, you’ll work with popular machine learning libraries such as TensorFlow and Keras, PyTorch and Coach.

💡 Coach is a Python library used alongside TensorFlow that collects statistics from the training process. It allows testing in robotics, games and more.

GRAB YOUR COPY OF DEEP LEARNING ILLUSTRATED

Learn Keras: Conclusion

Today we looked at the best ways to learn Keras including:

🔥 Best Course 🔥
A Beginner’s Guide to Deep Learning: Educative.io

💥 Best Book 💥
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

So whether you prefer courses or books, we think they’re both great ways to learn Keras.

Data scientists are also reading:

What is the best way to learn Keras?
We think the best way to learn Keras is with courses or books. One of the best Keras courses we could find is A Beginner’s Guide to Deep Learning on Educative.io. And one of the best books we found is Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow.
What is Keras?
Keras is a high-level neural network Python library that acts as an interface for the TensorFlow library. It’s used for developing and evaluating deep learning models.
Is there a DataCamp Keras course?
DataCamp has a Keras course titled Introduction to Deep Learning with Keras. You’ll learn about regression , classification and neural networks using real-world exercises such as predicting asteroid trajectories and reconstructing noisy images.