Keras courses can help you learn neural network design, model training, and performance evaluation techniques. You can build skills in optimizing hyperparameters, implementing convolutional and recurrent layers, and using transfer learning for various applications. Many courses introduce tools like TensorFlow and Python, that support developing AI models and deploying them in practical work.

Skills you'll gain: Keras (Neural Network Library), Deep Learning, Transfer Learning, Artificial Neural Networks, Recurrent Neural Networks (RNNs), Convolutional Neural Networks, Machine Learning Methods, Image Analysis, Autoencoders, Classification And Regression Tree (CART), Regression Analysis, Network Architecture, Natural Language Processing, Machine Learning, Model Evaluation
Intermediate · Course · 1 - 3 Months

Skills you'll gain: Transfer Learning, PyTorch (Machine Learning Library), Model Evaluation, Vision Transformer (ViT), Keras (Neural Network Library), Deep Learning, Reinforcement Learning, Convolutional Neural Networks, Unsupervised Learning, Autoencoders, Artificial Neural Networks, Recurrent Neural Networks (RNNs), Machine Learning Methods, Generative AI, Generative Adversarial Networks (GANs), Logistic Regression, Tensorflow, Artificial Intelligence and Machine Learning (AI/ML), Image Analysis, Data Preprocessing
Intermediate · Professional Certificate · 3 - 6 Months

Skills you'll gain: Generative Adversarial Networks (GANs), Exploratory Data Analysis, Model Deployment, Keras (Neural Network Library), NumPy, Transfer Learning, PyTorch (Machine Learning Library), Predictive Modeling, Matplotlib, Data Analysis, Artificial Intelligence, Data Preprocessing, Development Environment, Pandas (Python Package), Deep Learning, Classification And Regression Tree (CART), Artificial Neural Networks, Image Analysis, Machine Learning, Data Science
Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Keras (Neural Network Library), Reinforcement Learning, Convolutional Neural Networks, Unsupervised Learning, Deep Learning, Autoencoders, Tensorflow, Recurrent Neural Networks (RNNs), Machine Learning Methods, Generative AI, Generative Adversarial Networks (GANs), Transfer Learning, Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), Computer Vision, Model Evaluation, Performance Tuning
Intermediate · Course · 1 - 3 Months

Skills you'll gain: Embeddings, Natural Language Processing, Keras (Neural Network Library), Generative AI, Convolutional Neural Networks, Transfer Learning, Recurrent Neural Networks (RNNs), Model Evaluation, Image Analysis, Artificial Neural Networks, Text Mining, Computer Vision, Data Preprocessing, Tensorflow, Deep Learning, Model Deployment, Applied Machine Learning, Google Cloud Platform, Jupyter, Matplotlib
Beginner · Specialization · 1 - 3 Months

Skills you'll gain: Prompt Engineering, Apache Spark, Large Language Modeling, Transfer Learning, PyTorch (Machine Learning Library), Model Evaluation, Computer Vision, Retrieval-Augmented Generation, Unsupervised Learning, Generative Model Architectures, Generative AI, PySpark, Vision Transformer (ViT), Keras (Neural Network Library), LLM Application, Supervised Learning, Vector Databases, Machine Learning, Python Programming, Data Science
Build toward a degree
Intermediate · Professional Certificate · 3 - 6 Months

Skills you'll gain: Recurrent Neural Networks (RNNs), Transfer Learning, Tensorflow, Artificial Neural Networks, Embeddings, Keras (Neural Network Library), Deep Learning, Time Series Analysis and Forecasting, Image Analysis, Classification Algorithms, Convolutional Neural Networks, Natural Language Processing, Computer Vision, Forecasting, Supervised Learning, Machine Learning Algorithms, Machine Learning, Predictive Analytics, Model Evaluation, Predictive Modeling
Intermediate · Specialization · 1 - 3 Months

Imperial College London
Skills you'll gain: Tensorflow, Recurrent Neural Networks (RNNs), Autoencoders, Generative Model Architectures, Data Pipelines, Keras (Neural Network Library), Model Evaluation, Deep Learning, Image Analysis, Transfer Learning, Convolutional Neural Networks, Applied Machine Learning, Bayesian Statistics, Supervised Learning, Natural Language Processing, Computer Vision, Model Deployment, Artificial Neural Networks, Data Preprocessing, Probability Distribution
Intermediate · Specialization · 3 - 6 Months

Multiple educators
Skills you'll gain: Tensorflow, Keras (Neural Network Library), Machine Learning Methods, Model Evaluation, Machine Learning, Google Cloud Platform, Machine Learning Algorithms, Applied Machine Learning, Financial Trading, Reinforcement Learning, Recurrent Neural Networks (RNNs), Supervised Learning, Data Pipelines, Time Series Analysis and Forecasting, Statistical Machine Learning, Technical Analysis, Deep Learning, Securities Trading, Portfolio Management, Artificial Intelligence and Machine Learning (AI/ML)
Intermediate · Specialization · 1 - 3 Months

LearnQuest
Skills you'll gain: Data Preprocessing, Feature Engineering, Model Evaluation, Bioinformatics, Exploratory Data Analysis, Random Forest Algorithm, Pandas (Python Package), Scikit Learn (Machine Learning Library), Applied Machine Learning, Data Manipulation, Dimensionality Reduction, Data Cleansing, Keras (Neural Network Library), Data Transformation, NumPy, Classification Algorithms, Tensorflow, Artificial Neural Networks, Machine Learning, Data Science
Beginner · Specialization · 3 - 6 Months

Skills you'll gain: Transfer Learning, Keras (Neural Network Library), Embeddings, Deep Learning, Image Analysis, Computer Vision, Natural Language Processing, Data Preprocessing
Intermediate · Course · 1 - 4 Weeks

Skills you'll gain: Tensorflow, Convolutional Neural Networks, Keras (Neural Network Library), Matplotlib, Artificial Neural Networks, Image Analysis, Deep Learning, Applied Machine Learning, Python Programming, Model Evaluation, Adaptability, Problem Solving
Intermediate · Guided Project · Less Than 2 Hours
Keras is an open-source software library that provides a user-friendly interface for building and training deep learning models. It is built on top of TensorFlow and simplifies the process of creating complex neural networks. Keras is important because it allows developers and data scientists to prototype and experiment with deep learning models quickly, making it accessible for those who may not have extensive programming backgrounds. Its simplicity and flexibility have made it a popular choice in both academic and industry settings.‎
With skills in Keras, you can pursue various job roles in the tech industry. Common positions include machine learning engineer, data scientist, AI researcher, and deep learning engineer. These roles often involve developing algorithms and models that can analyze data, make predictions, and improve decision-making processes. As organizations increasingly rely on data-driven insights, the demand for professionals skilled in Keras and deep learning continues to grow.‎
To effectively learn Keras, you should focus on several key skills. First, a solid understanding of Python programming is essential, as Keras is primarily used with this language. Additionally, knowledge of machine learning concepts, neural networks, and data preprocessing techniques will be beneficial. Familiarity with TensorFlow, the underlying framework for Keras, is also important. Finally, hands-on experience with building and training models will help reinforce your learning.‎
Some of the best online courses for learning Keras include the Deep Learning with Keras and Tensorflow course, which provides a comprehensive introduction to deep learning concepts. The Introduction to Deep Learning & Neural Networks with Keras course is also highly recommended for beginners. For those looking to specialize further, the Keras Deep Learning & Generative Adversarial Networks (GAN) Specialization offers an in-depth exploration of advanced topics.‎
Yes. You can start learning keras on Coursera for free in two ways:
If you want to keep learning, earn a certificate in keras, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎
To learn Keras effectively, start by familiarizing yourself with Python and the basics of machine learning. Then, explore online courses that focus on Keras, such as those mentioned earlier. Practice by building simple models and gradually increase complexity as you gain confidence. Engaging with community forums and participating in projects can also enhance your learning experience and provide valuable insights.‎
Keras courses typically cover a range of topics, including the fundamentals of neural networks, model architecture, training and evaluation techniques, and practical applications of deep learning. You may also learn about advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). These topics equip you with the knowledge needed to tackle real-world problems using deep learning.‎
For training and upskilling employees or the workforce in Keras, the IBM Deep Learning with PyTorch, Keras and Tensorflow Professional Certificate is an excellent choice. It provides a structured learning path that covers essential concepts and practical skills. Additionally, the Deep Learning with Keras and Practical Applications course offers hands-on experience that can be beneficial for teams looking to implement deep learning solutions.‎